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Cited by 96Structural Health Monitoring TechniquesUltrasonics and Acoustic Wave PropagationStructural Engineering and Materials Analysis

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

Open access
Keshmiry, Ayoub, Hassani, Sahar, Mousavi, Mohsen, Dackermann, Ulrike·2023·Buildings
DOI 10.3390/buildings13040918
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Abstract

The development of Structural Health Monitoring (SHM) and Non-Destructive Testing (NDT) techniques has rapidly evolved and matured over the past few decades. Advances in sensor technology have facilitated deploying SHM systems for large-scale structures and local NDT of structural members. Although both methods have been successfully applied to identify structural damage in various systems, Environmental and Operational Condition (EOC) variations can influence sensor measurements and mask damage signatures in the structural response. EOCs include environmental conditions, such as temperature, humidity, and wind, as well as operational conditions, such as mass loading, vibration, and boundary conditions. The effect of EOCs can significantly undermine the reliability and robustness of damage assessment technologies and limit their performance. Thus, successful SHM and NDT systems can compensate for changing EOCs. This paper provides a state-of-the-art review of the effects of EOCs on SHM and NDT systems. It presents recent developments in advanced sensing technology, signal processing, and analysis techniques that aim to eliminate the masking effect of EOC variations and increase the damage sensitivity and performance of SHM and NDT systems. The paper concludes with current research challenges, trends, and recommendations for future research directions.

AI snapshot

Written from the paper’s full text.

TL;DR

This review synthesizes how environmental and operational condition (EOC) variations—temperature, humidity, wind, mass loading, and boundary conditions—affect structural health monitoring (SHM) and non-destructive testing (NDT), often masking damage signatures and reducing reliability. It surveys sensing technologies and data-analysis methods for EOC compensation and identifies future research needs, especially for combined EOC effects and non-temperature factors.

Key findings

  • EOCs can alter structural responses and sensor readings enough to mask damage or cause false damage identifications; temperature is the most widely studied EOC, affecting stiffness, boundary conditions, natural frequencies, mode shapes, damping, and sensor signals such as electromechanical admittance and impedance.
  • A comparative analysis using a weigh-in-motion roadway scale on a steel bridge reported that thermal stresses are significantly more influential than stresses induced by traffic.
  • Wind affects long-span bridges by influencing aerodynamic coupling, modal parameters, and response amplitude; high winds make modal parameter identification difficult and uncertain, and wind-induced vibrations can cause flutter or buffet when they contribute more energy than damping.
  • Mass loading from traffic decreases natural frequencies; short-span bridges are significantly more affected by traffic-induced mass variations than by environmental stiffness changes, while for middle-to-long-span bridges traffic-induced natural frequency variations are insignificant. Humidity had limited effects on modal parameters of a 600 m supertall building monitored over two years and five typhoons.

Methods

  • Design: state-of-the-art/systematic review of the effects of EOCs on SHM and NDT systems, covering sensing technologies, signal processing, and data-analysis techniques for EOC compensation.
  • Data: published literature; the paper reports that a Google Scholar search on “structural health monitoring” completed in March 2023 found over 20,000 papers published in the last two decades, and it summarizes studies in tables on numerical methods, EOC effects, NDT approaches, and data-analysis algorithms.
  • Analysis: narrative synthesis with summary tables; no formal meta-analysis or PRISMA-style systematic search protocol is described in the text.

Limitations

  • Stated: most compensation techniques focus on temperature, while other EOCs such as traffic and wind are unavoidable but less addressed; moisture and applied loads have not been adequately examined in practice; baseline-free methods can be affected by EOC variations; AI/ML approaches require better input datasets; and only minor work has addressed combined effects of multiple EOC factors.
  • Evident: despite the title “systematic review,” the text does not report inclusion/exclusion criteria, a reproducible search strategy, or quality appraisal of included studies.
  • Evident: many findings are drawn from individual case studies or laboratory experiments, and the paper notes challenges such as lack of standardization and dependence on material properties, which may limit generalizability.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/buildings13040918

Cites

267 of 268 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

87 of 148 cited sources checked · 33 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Active SHM enables direct and targeted detection and evaluation of structural damage, resulting in more reliable and accurate health assessment.

    “On the other hand, active SHM enables the direct and targeted detection and evaluation of structural damage resulting in more reliable and accurate health assessment.”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

      Section 6, “Smart Structures” (page not specified), describes actuator-and-sensor systems that induce vibrations, measure the response, and analyze it to identify and characterize damage. That supports the detection and evaluation part of the claim, but the copy does not establish that active SHM produces more reliable or accurate health assessments. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [59] Amer, 2022, Statistical guided-waves-based structural health monitoring via stochastic non-parametric time series models, Struct. Health Monit. source

      The Introduction (p. 3) describes active-sensing guided-wave SHM methods for detecting and quantifying damage. But the Abstract (p. 1) attributes accurate, robust detection to this paper’s proposed statistical framework, while noting limitations in existing approaches. The source does not establish that active SHM generally produces more reliable and accurate health assessments. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [60] Feng, T., and Aliabadi, M. (2021). Structural Integrity Assessment of Composites Plates with Embedded PZT Transducers for Structural Health Monitoring. Materials, 14. source

      The Introduction describes SHM as providing real-time damage detection, and §3.1 says the electro-mechanical impedance method can evaluate local damage severity. These passages support detection and targeted evaluation, but do not establish that active SHM produces a more reliable or accurate health assessment. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    Integrating multiple NDT techniques enhances accuracy.

    “By integrating multiple techniques, additional parameters can be evaluated and added to the measurement process, which in turn enhances accuracy [112,113].”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [112] Tayeh, B., Hadzima-Nyarko, M., Riad, M.Y.R., and Hafez, R.D.A. (2023). Behavior of Ultra-High-Performance Concrete with Hybrid Synthetic Fiber Waste Exposed to Elevated Temperatures. Buildings, 13. source

      Section 2.3.1 describes using ultrasonic pulse velocity to estimate damage and residual compressive strength, and the Abstract lists several measurements. Neither passage says that integrating multiple NDT techniques adds parameters or improves accuracy; searches for that proposition found no supporting passage in our copy. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
    • cites [113] Dackermann, 2016, A comparative study of using static and ultrasonic material testing methods to determine the anisotropic material properties of wood, Constr. Build. Mater. source
  • Premisecitation

    Moisture absorption can alter material properties, potentially leading to false damage features and incorrect damage identifications.

    “Due to the absorption of moisture, material properties can be altered, potentially leading to false damage features and incorrect damage identifications [125].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [125] Bremer, 2016, Fibre optic sensors for the structural health monitoring of building structures, Procedia Technol. source

      Sections 2.1 and 2.2 describe sensor materials swelling when exposed to water and the resulting changes in optical readings. They do not say that moisture alters the structural material’s properties or produces false damage features or incorrect damage identifications. No page numbers are available in our copy. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
  • Premisecitation

    Temperature and structural responses are strongly correlated.

    “Temperature and structural responses are strongly correlated [149,150].”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [149] Antunes, R.A., Cortez, N.E., Gianesini, B.M., and Vieira Filho, J. (2019). Modeling, simulation, experimentation, and compensation of temperature effect in impedance-based SHM systems applied to steel pipes. Sensors, 19. source
    • cites [150] Chen, Y., and Xue, X. (2018). Advances in the structural health monitoring of bridges using piezoelectric transducers. Sensors, 18. source

      Section 4.4, “Compensating Temperature Effects” (page not provided), says bridge dynamic characteristics are temperature-dependent and that temperature-related changes can mask changes caused by damage. This supports a relationship between temperature and structural responses, but the paper does not establish that they are strongly correlated. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    High wind conditions have a complex effect on dynamic structural properties.

    “Studies found that high wind conditions have a complex effect on dynamic structural properties [129,175-177].”
    Quote in the paperNot a substantial claim2 of 4 sources checked
    • cites [129] Kolios, 2016, Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm, Renew. Sustain. Energy Rev. source

      Section 3.2 (page unavailable) says variations in environmental and operational conditions affect structural dynamic behavior and can alter measured properties. Section 2.2.4 (page unavailable) also describes uncertainty from wind and wave loading, but the copy does not specifically establish the effect of high wind conditions on dynamic structural properties. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [175] Li, 2016, State-of-the-art in structural health monitoring of large and complex civil infrastructures, J. Civ. Struct. Health Monit. source
    • cites [176] Xu, Y.L., and Xia, Y. (2019). Structural Health Monitoring of Long-Span Suspension Bridges, CRC Press.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [177] Chen, Z., Zhou, X., Wang, X., Dong, L., and Qian, Y. (2017). Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study. Sensors, 17. source

      Section 2.2 describes complex wind-induced vibrations in long-span bridges, including buffeting, vortex shedding and flutter. Section 3.4 says high wind speeds can make measured wind speed non-stationary, but it does not establish how high winds affect the bridge’s dynamic structural properties. No page numbers are available in our copy. (LLM, conf=0.79)

      Experimental — accuracy not yet measured
  • May combine two claims. 28
  • Classified as probably not a substantial claim. 73
  • The quote only partly states this claim. 5

  • May combine two claims.

    Premisecitation

    Passive SHM methods can be successful for specific applications but lack accuracy, and damage might stay undetected.

    “While passive methods can be successful for specific applications, they lack accuracy, and damage might stay undetected [56,57].”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [56] Lackner, 2011, Passive structural control of offshore wind turbines, Wind Energy source
    • cites [57] Capineri, L., and Bulletti, A. (2021). Ultrasonic guided-waves sensors and integrated structural health monitoring systems for impact detection and localization: A review. Sensors, 21. source

      Section 3 describes passive piezoelectric sensing as a common method for detecting impacts, supporting its use in a specific application. Sections 2.4 and 4.2 discuss sources of impact-localization error and ways to improve accuracy, but do not say passive methods generally lack accuracy. The paper distinguishes passive impact detection from active damage evaluation; it does not establish that damage might remain undetected by passive methods. (LLM, conf=0.78)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Wind conditions such as wind speed and turbulence affect turbine vibration levels.

    “They found that wind conditions like wind speed and turbulence affect turbine vibration levels.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [180] Weijtjens, 2017, Vibration based structural health monitoring of the substructures of five offshore wind turbines, Procedia Eng. source

      Section 2.1, “Wake effects,” reports that turbulent air in turbine wakes increases vibration levels, supporting the turbulence part of the claim. The passages on wake effects and operating conditions do not establish an effect of wind speed on vibration levels; rotational speed is not the same measure. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Marine growth adversely affects offshore structures by roughening surfaces, increasing drag coefficient, and causing changing mass loads.

    “Offshore structures are known to be adversely affected by marine growth.The roughened surfaces can increase the drag coefficient of the structure.Moreover, this phenomenon causes changing mass loads to the structures.Therefore, this challenge should be considered in SHM systems of marine structures [129].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [129] Kolios, 2016, Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm, Renew. Sustain. Energy Rev. source

      Section 2.1, “History and evolution” (page unavailable), identifies changing mass caused by marine growth as a practical difficulty in monitoring offshore platforms. The passages found do not say that marine growth roughens surfaces or increases drag coefficient, so those attributed effects are unsupported by our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A lightweight diagnostic film for metal and composite structures provided durable and reliable performance in withstanding variable harsh humidity and temperature.

    “The film provided durable and reliable performance in withstanding the variable harsh humidity and temperature.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [193] Bekas, D.G., Sharif-Khodaei, Z., and Aliabadi, M.F. (2018). An innovative diagnostic film for structural health monitoring of metallic and composite structures. Sensors, 18. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A Bayesian probabilistic approach for characterizing wind-induced responses of high-rise structures enabled accounting for uncertainty in monitored responses.

    “This approach enabled accounting for the uncertainty in the monitored responses.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [186] Wang, 2022, Bayesian probabilistic assessment of occupant comfort of high-rise structures based on structural health monitoring data, Mech. Syst. Signal Process. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A reliability assessment approach for bridge structures is capable of eliminating temperature and humidity effects.

    “This method is capable of eliminating temperature and humidity effects.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [195] He, X., Tan, G., Chu, W., Zhang, S., and Wei, X. (2022). Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated. Sustainability, 14. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The simulated varying wind condition satisfied the characteristic of field data.

    “The simulated varying wind condition satisfied the characteristic of field data.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [184] Zhu, L., McCrum, D., Sweeney, C., and Keenahan, J. (2023). Full-scale computational fluid dynamics study on wind condition of the long-span Queensferry Crossing Bridge. J. Civ. Struct. Health Monit., 1–18. source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    SVM is beneficial for pattern recognition and can be used for regression analysis.

    “The technique is beneficial for pattern recognition and can be used for regression analysis [242-244].”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [242] Yang, Y., Sun, W., and Su, G. (2022). A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis. Buildings, 12. source
    • cites [243] Lei, 2022, Structural damage identification method based on vibration statistical indicators and support vector machine, Adv. Struct. Eng. source
    • cites [244] Dackermann, U., Yu, Y., Niederleithinger, E., Li, J., and Wiggenhauser, H. (2017). Condition assessment of foundation piles and utility poles based on guided wave propagation using a network of tactile transducers and support vector machines. Sensors, 17. source

      Section 5 describes using SVM to classify pole-damage patterns, and Section 6 reports strong classification results. The paper does not discuss using SVM for regression analysis; searches for “regression analysis” and “support vector regression” found no supporting passage. Page numbers were not available in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    ML algorithms can interpret signals or images to analyze, inspect, and examine material integrity.

    “They can interpret signals or images to analyze, inspect, and examine material integrity [248-250].”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [248] Furuya, 2019, Imagification technology and deep learning accelerating defect detection in non-destructive testing for wind turbine blades, Fujitsu Sci. Tech. J.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [249] Yu, 2019, Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory, Struct. Health Monit. source
    • cites [250] Kot, P., Muradov, M., Gkantou, M., Kamaris, G.S., Hashim, K., and Yeboah, D. (2021). Recent advancements in non-destructive testing techniques for structural health monitoring. Appl. Sci., 11. source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AANN is typically used to simulate a nonlinear PCA process and solve problems related to feature extraction, pattern recognition, and dimensionality reduction.

    “The program is typically used to simulate a nonlinear PCA process and solve problems related to feature extraction, pattern recognition, and dimensionality reduction [261,262].”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [261] Guerrisi, G., Del Frate, F., and Schiavon, G. (2022). Satellite On-Board Change Detection via Auto-Associative Neural Networks. Remote Sens., 14. source

      The Introduction (page not supplied) says AANNs have been used for dimensionality reduction and feature extraction from remote-sensing images. The Conclusions describe this paper's use of extracted features for change detection. Our copy does not describe AANNs as simulating nonlinear PCA or identify pattern recognition as an application. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [262] Entezami, A., De Michele, C., Arslan, A.N., and Behkamal, B. (2022). Detection of partially structural collapse using long-term small displacement data from satellite images. Sensors, 22. source

      Section 2.2.1 (page not provided) describes the auto-associative neural network as mapping feature samples through a nonlinear function into a lower-dimensional space, supporting the dimensionality-reduction aspect. The paper also uses it for feature normalization, but does not describe pattern recognition as an AANN application or state that AANN is typically used to simulate nonlinear PCA; nonlinear PCA appears in a cited reference’s title. (LLM, conf=0.83)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    ANN approximates the nonlinear function between inputs and outputs with high accuracy using historical data.

    “Using historical data, it approximates the nonlinear function between inputs and outputs with high accuracy [245-247].”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [245] Ding, 2022, Simultaneous identification of structural damage and nonlinear hysteresis parameters by an evolutionary algorithm-based artificial neural network, Int. J. Non-Linear Mech. source
    • cites [246] Albahri, 2022, Hybrid artificial neural network and structural equation modelling techniques: A survey, Complex Intell. Syst. source

      The Introduction (page unavailable) says ANN captures nonlinear correlations among variables and provides more accurate results. It does not specifically say that ANN approximates an input–output function using historical data, or establish the unqualified claim of high accuracy. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [247] Dackermann, 2010, Dynamic-based damage identification using neural network ensembles and damage index method, Adv. Struct. Eng. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Damage-detection strategies for health monitoring can be categorized into model-based and data-based methods.

    “Damage-detection strategies for health monitoring can be categorized into model-based and data-based methods [264].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [264] Moallemi, A., Burrello, A., Brunelli, D., and Benini, L. (2021, January 17–20). Model-based vs. data-driven approaches for anomaly detection in structural health monitoring: A case study. Proceedings of the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Scotland, UK. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AANN is an ANN structure with the same input and output layer.

    “AANN is an ANN structure with the same input and output layer.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [261] Guerrisi, G., Del Frate, F., and Schiavon, G. (2022). Satellite On-Board Change Detection via Auto-Associative Neural Networks. Remote Sens., 14. source
    • cites [262] Entezami, A., De Michele, C., Arslan, A.N., and Behkamal, B. (2022). Detection of partially structural collapse using long-term small displacement data from satellite images. Sensors, 22. source

  • May combine two claims.

    Premisecitation

    Swarm intelligent algorithms are widely used for structural optimization and compensating for EOCs.

    “Swarm intelligent algorithms are widely used for structural optimization and compensating for EOCs.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [237] Luo, J., Huang, M., and Lei, Y. (2022). Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review. Buildings, 12. source

      The Optimization Tools section surveys numerous swarm-based methods for structural damage identification, including particle swarm and cuckoo search, and describes applications that account for ambient temperature. The cited review supports their use in this setting, but its passages address temperature effects rather than compensation for environmental and operational conditions (EOCs) generally. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites [238] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement. Sensors, 23. source

      Section 4.1 describes swarm-based algorithms used for structural health monitoring and optimal sensor placement, supporting their use in structural optimization problems. Section 2.3 lists compensation for EOC changes as a sensor characteristic, but does not connect that compensation to swarm-intelligence algorithms. The claim that those algorithms are used to compensate for EOCs is therefore unsupported by this copy. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In composite materials, Fiber crack has a weak influence on mass.

    “| Mass | - | - | - | - | - | + | + | - | - |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

      Section 2.2 says fiber cracks strongly affect stiffness and moisture strongly affects mass, but it does not state how fiber cracks affect mass. Searches for the Table 3 mass–fiber-crack entry did not retrieve that relationship from our copy. (LLM, conf=0.72)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In composite materials, Delamination has a weak influence on mass.

    “| Mass | - | - | - | - | - | + | + | - | - |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

  • The quote only partly states this claim.

    Premisecitation

    In composite materials, Mechanical Load has a weak influence on boundary formation.

    “| Boundary formation | + | - | - | + | - | • | - | - | - |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In composite materials, Fiber crack has a weak influence on boundary formation.

    “| Boundary formation | + | - | - | + | - | • | - | - | - |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

      Section 2.2 says boundary formation is least influenced by temperature and humidity, but does not state how fiber cracks affect it. A search for “boundary formation” found no passage in our copy establishing the claimed weak influence of fiber cracks. (LLM, conf=0.70)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In composite materials, Matrix crack has a weak influence on mass.

    “| Mass | - | - | - | - | - | + | + | - | - |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

      Section 3.2 says mass is the least sensitive structural property to damage, and the Introduction identifies matrix cracking as a damage mechanism. The copy does not specifically establish how strongly matrix cracks affect mass, so the claim is more specific than the evidence. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced sensing systems have automatability.

    “• | automatability, | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced sensing systems are less labor intensive.

    “• | less labor intensivity, | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 4.4 says wireless sensing avoids labor-intensive cabling and UAV sensors reduce logistics and working hours. These examples support lower labor demands for particular advanced sensing systems, but the review does not establish that advanced sensing systems are generally less labor intensive. (LLM, conf=0.83)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced sensing systems are more cost-effective.

    “• | more cost-effectiveness. | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 3.3 says advances in ultrasonic sensor technology have produced more cost-effective sensors, and Section 4.4 says wireless sensing avoids cost-intensive cabling. These examples support a cost benefit for particular technologies, but not the unqualified claim that advanced sensing systems generally are more cost-effective. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims. The quote only partly states this claim.

    Premisecitation

    For SHM systems, temperature is monitored by temperature sensors, fiber optic sensors, thermocouples, and barometers.

    “| | | -Fiber optic sensors |”
    • Also stated in: § - · plain
      “| Temperature | -Temperature sensors | |”
    • Also stated in: § - · plain
      “| | | -Thermocouples |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 4.4.13, “Temperature Sensors” (page unavailable), identifies thermocouples and fiber optic sensors among the sensors used to measure temperature for SHM. It does not identify barometers as temperature-monitoring sensors, so that item is unsupported. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims. The quote only partly states this claim.

    Premisecitation

    For SHM systems, wind is monitored by hygrometers, ultrasonic and propeller anemometers, and visibility and precipitation sensors.

    “| Wind | -Hygrometers -Ultrasonic and propeller anemometers | |”
    • Also stated in: § - · plain
      “| | | -Visibility and precipitation sensors |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 4.2 identifies wind as an environmental factor relevant to SHM and says Table 15 lists sensors for measuring such conditions. Section 4.4.1 also identifies wind as an ambient property measured in SHM. The retrieved passages do not identify hygrometers, ultrasonic or propeller anemometers, or visibility and precipitation sensors as wind-monitoring devices. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced sensing systems have capability for remote sensing.

    “• | capability for remote sensing, | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims. The quote only partly states this claim.

    Premisecitation

    For SHM systems, highway traffic is monitored by static/dynamic strain gauges, dynamic weigh-in-motion stations, and high-definition video cameras.

    “| Highway traffic | -Dynamic weigh-in-motion stations | |”
    • Also stated in: § - · plain
      “| | | -High-definition video cameras |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 4.2 discusses how traffic loading affects structures, and Section 4.4 describes SHM sensors generally. Neither passage says highway traffic is monitored by dynamic weigh-in-motion stations; searches for “weigh-in-motion” and “WIM” found no such passage in our copy. (LLM, conf=0.70)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Advanced sensing systems have more accuracy and reliability.

    “• | more accuracy and reliability, | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 3.3 reports improved accuracy for ultrasonic sensors, while Section 4.4 describes improved reliability for sensor networks and vision-based sensors. These examples support the general direction of the claim, but the review does not establish that advanced sensing systems as a whole have both greater accuracy and greater reliability; it also notes accuracy limitations for vision-based sensors. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced sensing systems have less sensitivity to EOC variations.

    “• | less sensitivity to EOC variations, | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 4.2 describes sensitivity to EOC variations as a significant challenge for SHM systems and says EOC-robust sensors would be advantageous. Section 4.4 says independence from environmental and operational variations is an ideal, but does not establish that advanced sensing systems have less sensitivity to those variations. (LLM, conf=0.72)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Advanced sensing systems are suitable for continuous monitoring.

    “• | suitability for continuous monitoring, | |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    For SHM systems, railway traffic is monitored by static/dynamic strain gauges and high-definition video cameras.

    “| Railway traffic | -Static/dynamic strain gauges -High-definition video cameras | |”
    • Also stated in: § - · plain
      “| | | -Static/dynamic strain gauges |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [141] Hassani, S., and Dackermann, U. (2023). A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring. Sensors, 23. source

      Section 4.4 (page unavailable) identifies strain gauges and vision-based cameras as SHM sensors, and Section 4 says SHM can be applied to railway tracks. The passages do not say that railway traffic is monitored with static/dynamic strain gauges and high-definition video cameras specifically. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • The quote only partly states this claim.

    Premisecitation

    In composite materials, Mechanical Load has a weak influence on material stiffness.

    “| Material stiffness | • | • | + | • | + | - | + | - | - |”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [58] Hassani, S., Mousavi, M., and Gandomi, A.H. (2022). Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors, 22. source

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Successful SHM and NDT systems can compensate for changing EOCs.

    “Thus, successful SHM and NDT systems can compensate for changing EOCs.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    Environmental and Operational Condition (EOC) variations can influence sensor measurements and mask damage signatures in the structural response.

    “Although both methods have been successfully applied to identify structural damage in various systems, Environmental and Operational Condition (EOC) variations can influence sensor measurements and mask damage signatures in the structural response.”
    • Also stated in: § Electromagnetic · plain
      “EOCs can mask the signature of damage in the structural responses in both passive and active assessment techniques.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Overlooking EOC influences can affect damage detection accuracy and lead to unreliable conclusions.

    “By overlooking these influences, damage detection accuracy may be affected and lead to unreliable conclusions.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    Wind loadings influence the aerodynamic coupling between the structure and wind, modal parameters, and response amplitude.

    “These loadings influence the aerodynamic coupling between the structure and wind, modal parameters, and response amplitude.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Bridge systems with long spans are particularly susceptible to wind loads, and high winds can significantly affect these structures.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Thermal stresses are significantly more influential than stresses induced by traffic on a steel bridge.

    “It was reported that thermal stresses are significantly more influential than stresses induced by traffic [131].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [131] Helmicki, A., Hunt, V., Shell, M., Lenett, M., Turer, A., Dalal, V., and Aktan, A. (1999, January 8–10). Multidimensional performance monitoring of a recently constructed steel-stringer bridge. Proceedings of the 2nd International Workshop on Structural Health Monitoring, Stanford, CA, USA.

  • May combine two claims.

    Premisecitation

    Varying EOCs have a significant effect on damage signatures and are recognized as a major concern for the reliability and accuracy of current SHM systems.

    “As outlined above, varying EOCs have a significant effect on damage signatures and have been recognized by the research community as a major concern for the reliability and accuracy of current SHM systems [140].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [140] Chen, H.P. (2018). Structural Health Monitoring of Large Civil Engineering Structures, John Wiley & Sons. source

  • May combine two claims.

    Interpretationreasoning

    Civil structures under EOCs often exhibit inherent non-stationary dynamic responses, and quasi-static signals can obscure shifts in measured structural response due to damage.

    “Civil structures under EOCs often exhibit inherent non-stationary dynamic responses, and quasi-static signals can obscure any shifts in the measured structural response due to damage.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    EOCs can influence various material and structural properties, such as stiffness and boundary conditions, as well as vibration characteristics, including natural frequencies, mode shapes, and damping.

    “EOCs can influence various material and structural properties, such as stiffness and boundary conditions, as well as vibration characteristics, including natural frequencies, mode shapes, and damping.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A frequency-modified method for continuous beam bridges can eliminate the effects of humidity and temperature on structural responses.

    “The proposed technique can eliminate the effects of humidity and temperature on structural responses.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [194] He, 2019, Frequency modification of continuous beam bridge based on co-integration analysis considering the effect of temperature and humidity, Struct. Health Monit. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Temperature affects material and vibration properties through solar radiation, day-night alternations, and seasonal shifts.

    “Temperature affects material and vibration properties in several ways, including solar radiation, day-night alternations, and seasonal shifts.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Under the same damage scenarios, temperature variations can increase the severity of structural damage.

    “Under the same damage scenarios, temperature variations can increase the severity of structural damage.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A data-driven algorithm for wind-excited structures was proposed based on long-term monitoring data of a cantilever traffic signal structure.

    “They proposed a data-driven algorithm for wind-excited structures based on the long-term monitoring data of this structure.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [187] Tsai, L.W., and Alipour, A. (2023). Structural health monitoring and fatigue life reliability assessment of a flexible structure in extreme wind. J. Civ. Struct. Health Monit., 1–15. source

  • May combine two claims.

    Interpretationreasoning

    Temperature affects the stiffness of elements and can impact a system's material properties and boundary conditions.

    “This is due to its effects on the stiffness of elements and their potential impact on a system's material properties and boundary conditions.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Ambient humidity has limited effects on the modal parameters of a 600 m high supertall building.

    “The authors indicated that ambient humidity has limited effects on the modal parameters of the structure.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [114] Zhou, 2023, Investigation of modal parameters of a 600-m-tall skyscraper based on two-year-long structural health monitoring data and five typhoons measurements, Eng. Struct. source

  • May combine two claims.

    Interpretationreasoning

    In high winds, accurately identifying a bridge's modal parameters is difficult and their estimates are subject to substantial uncertainty.

    “In general, in high winds, it is difficult to accurately identify a bridge's modal parameters, and their estimates are subject to a substantial degree of uncertainty.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The recommended equivalent static wind load approach is practical for wind resistance design, but displacement responses were slightly higher than actual responses.

    “According to the study, the recommended approach is practical for wind resistance design; however, displacement responses were slightly higher than actual responses.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [181] Li, 2022, Wind-induced response monitoring of large-span air-supported membrane structure coal-shed under the influence of typhoons, Thin-Walled Struct. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The equivalent static wind load technique based on maximum displacement equivalence can be used to calculate wind-induced displacement responses.

    “They reported that the equivalent static wind load technique based on the fundamentals of maximum displacement equivalence could be used to calculate wind-induced displacement responses.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [181] Li, 2022, Wind-induced response monitoring of large-span air-supported membrane structure coal-shed under the influence of typhoons, Thin-Walled Struct. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    VMD is a time-frequency analysis strategy for analyzing non-stationary and nonlinear signals based on decomposing the original signal into several sets of intrinsic mode functions.

    “VMD is a time-frequency analysis strategy for analyzing non-stationary and nonlinear signals based on decomposing the original signal into several sets of intrinsic mode functions (IMF) [53,263].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [53] Hassani, 2022, Damage detection of composite laminate structures using VMD of FRF contaminated by high percentage of noise, Compos. Struct. source
    • cites [263] Ji, 2022, Structural performance degradation identification of offshore wind turbines based on variational mode decomposition with a Grey Wolf Optimizer algorithm, Ocean Eng. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A linear relationship exists between temperature and structural response.

    “A linear relationship exists between temperature and structural response.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [255] Vieira, 2022, Insights on the impact of structural health monitoring systems on the operation and maintenance of offshore wind support structures, Struct. Saf. source
    • cites [256] Li, 2022, A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques, Struct. Health Monit. source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Detecting damage requires sensor data that are sensitive to the damage to provide a direct correlation to the health of the structure.

    “Detecting damage requires sensor data that are sensitive to the damage to provide a direct correlation to the health of the structure.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Bridge vibration was the primary contributing factor to vertical vehicle vibration.

    “According to the results, bridge vibration was the primary contributing factor to the vertical vehicle vibration”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [206] Wang, 2019, A monitoring-based approach for evaluating dynamic responses of riding vehicle on long-span bridge under strong winds, Eng. Struct. source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Next-generation measurement technology for SHM has grown in response to the need for automated and efficient sensing systems.

    “Next-generation measurement technology for SHM has grown in response to the need for automated and efficient sensing systems.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    High cost and complexity of advanced sensing technologies render them difficult to implement on a large scale.

    “One of the main challenges is the high cost and complexity of these sensing technologies rendering them difficult to implement on a large scale.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    GA provides an effective solution to both constrained and unconstrained optimization problems by mimicking biological evolution through natural selection.

    “GA provides an effective solution to both constrained and unconstrained optimization problems by mimicking the process of biological evolution through natural selection.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [251] Ismail, 2021, Optimizing the placement of piezoelectric wafers on closed sections using a genetic algorithm–Towards application in structural health monitoring, Ultrasonics source
    • cites [252] Hassan, 2020, Failure prediction in self-sensing nanocomposites via genetic algorithm-enabled piezoresistive inversion, Struct. Health Monit. source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    Mass loading can decrease the natural frequencies of a structure, while cold temperatures can have an elevating effect.

    “While mass loading can decrease the natural frequencies of a structure, cold temperatures can have an elevating effect.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisenone given

    Advanced sensing techniques rely on emerging technologies based on multi-physics and include more complex system setups and data analysis methodologies.

    “As compared to conventional methods, advanced sensing techniques rely on emerging technologies based on multi-physics and include more complex system setups and data analysis methodologies”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    High humidity can affect the structure's weight and response to wind loads.

    “As another example, high humidity can affect the structure's weight and response to wind loads.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Lack of standardization in sensing techniques makes it difficult to compare and evaluate different technologies.

    “Another challenge is the lack of standardization in sensing techniques, which makes it difficult to compare and evaluate different technologies.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    In model-based approaches, damage detection techniques are based on initial physics models of a structure.

    “In model-based approaches, damage detection techniques are based on initial physics models of a structure.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Fiber optic sensors are already widely applied in several areas due to benefits such as small size, corrosion resistance, high precision, flexibility, lightweight, and anti-electromagnetic interference property.

    “These sensors are already widely applied in several areas due to their benefits, such as small size, corrosion resistance, high precision, flexibility, lightweight, and anti-electromagnetic interference property [214,215].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [214] Min, 2021, Optical fiber sensing for marine environment and marine structural health monitoring: A review, Opt. Laser Technol. source
    • cites [215] Sivasuriyan, 2021, Development of smart sensing technology approaches in structural health monitoring of bridge structures, Adv. Mater. Sci. Eng. source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    Most studies focus on evaluating the effect of EOC on long-term structural condition monitoring, and developed techniques are primarily designed for monitoring large-scale structures over a long period.

    “Most studies focus on evaluating the effect of EOC on long-term structural condition monitoring, and likewise, the developed techniques are primarily designed for monitoring large-scale structures over a long period.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Next-generation sensing techniques are outperforming conventional and advanced sensors.

    “The development of next-generation sensing techniques as a result of recent advancements in sensing and robotic technology is outperforming conventional and advanced sensors.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    Wind can cause structural vibrations, which can impact the accuracy of the sensors and can lead to false system estimations.

    “Wind can cause structural vibrations, which can impact the accuracy of the sensors and can lead to false systems estimations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Wind forces and bridge vibration were the dominant factors contributing to lateral and torsional vibration of the vehicle, respectively.

    “while wind forces and bridge vibration were the dominant factors contributing to the lateral and torsional vibration of the vehicle, respectively.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [206] Wang, 2019, A monitoring-based approach for evaluating dynamic responses of riding vehicle on long-span bridge under strong winds, Eng. Struct. source

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Nonlinear PCA and kernel Gaussian PCA can improve the chance of detecting damage and reduce false negative damage detection.

    “It was found that nonlinear PCA and kernel Gaussian PCA can improve the chance of detecting damage and reduce false negative damage detection.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [205] Rahim, S. (2018). Investigating the Effect of Variable Mass Loading in Structural Health Monitoring from a Machine Learning Perspective. [Ph.D. Thesis, University of Sheffield].

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    PCA can reduce the data dimension.

    “As a multivariate statistical analysis technique, PCA can reduce the data dimension.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [257] Cunha, 2012, Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection, Mech. Syst. Signal Process. source
    • cites [258] Cao, 2022, Robust damage localization in plate-type structures by using an enhanced robust principal component analysis and data fusion technique, Mech. Syst. Signal Process. source

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Humidity and mass-loading effects can induce changes in the structure's weight due to loading and unloading, resulting in changes in the sensors' reading and the SHM system's reliability.

    “Humidity and mass-loading effects can induce changes in the structure's weight due to loading and unloading, resulting in changes in the sensors' reading and the SHM system's reliability.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisenone given

    Some advanced sensing technologies require calibration and elaborate maintenance work, which can be time-consuming and expensive.

    “Additionally, some advanced sensing technologies require calibration and elaborate maintenance work, which can be time-consuming and expensive.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Data-based techniques are based on structural measurements.

    “Data-based techniques, on the other hand, are based on structural measurements.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    A sensor that is robust to variations in EOCs can be of significant advantage in accurately and reliably detecting damage in structures.

    “A sensor that is robust to variations in EOCs can be of significant advantage in accurately and reliably detecting damage in structures.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisenone given

    Temperature variations can result in structural expansion or contraction, leading to changes in the structure's mechanical characteristics, such as natural frequency and mode shapes.

    “For instance, temperature variations can result in structural expansion or contraction, leading to changes in the structure's mechanical characteristics, such as natural frequency and mode shapes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI techniques, including deep learning, are operated to detect hidden patterns in large data sets.

    “AI techniques, including deep learning, are operated to detect hidden patterns in large data sets [236].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [236] Olson, 2007, Data mining in business services, Serv. Bus. source

  • Classified as probably not a substantial claim.

    Premisereasoning

    Sensors can render misleading data when affected by EOC changes.

    “It has also been argued that sensors can render misleading data when affected by EOC changes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    Performance of advanced sensors can be affected by the structure's material properties, making it challenging to apply the same technology to different types of systems.

    “Furthermore, the performance of these sensors can be affected by the structure's material properties, making it challenging to apply the same technology to different types of systems.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Swarm intelligent algorithms assist in improving fitness functions in combinatorial and numerical optimization problems.

    “By discovering different combinations of values, these algorithms assist in improving fitness functions in combinatorial and numerical optimization problems [259,260].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [259] Das, 2021, Performance of swarm intelligence based chaotic meta-heuristic algorithms in civil structural health monitoring, Measurement source
    • cites [260] Jiang, 2022, Structural deformation prediction model based on extreme learning machine algorithm and particle swarm optimization, Struct. Health Monit. source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    ML has shown promise in addressing the drawbacks associated with current NDT methods.

    “ML has shown promise in addressing the drawbacks associated with current NDT methods.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    With applications of AI, ML, and Deep Learning algorithms, more advanced damage detection techniques have been proposed to tackle the effects of EOC variations on SHM and NDT methods.

    “With the applications of AI, ML, and Deep Learning (DL) algorithms, more advanced damage detection techniques have been proposed to tackle the effects of EOC variations on SHM and NDT methods.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Most of these techniques rely upon a predictive model whose prediction error is taken as a damage-sensitive feature.

    “On the other hand, most of these techniques rely upon a predictive model whose prediction error is taken as a damage-sensitive feature.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Most researchers have developed compensation techniques and strategies for temperature effects.

    “Most researchers have developed compensation techniques and strategies for temperature effects.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Premisenone given

    Addressing the limitations of the input dataset is required for training and algorithm accuracy.

    “Nevertheless, addressing the limitations of the input dataset is required for training and algorithm accuracy.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    SHM methods have not been adequately examined in practice for the effects of moisture and applied loads.

    “SHM methods have not been adequately examined in practice for the effects of moisture and applied loads.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    The second static-based method based on temperature-induced responses can apply the excitation by temperature in structure identification.

    “The second method is based on temperature-induced responses and can apply the excitation by temperature in structure identification.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    The nonlinear relationship between structural characteristics and temperature responses can cause model uncertainty.

    “Furthermore, the nonlinear relationship between structural characteristics and temperature responses can cause model uncertainty.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisenone given

    The first static-based method based on temperature-removed responses can efficiently analyze the results of other factors, such as traffic load, but may lead to the loss of critical information.

    “This method can efficiently analyze the results of other factors, such as traffic load; it may, however, lead to the loss of critical information.”
    Quote in the paperNot a substantial claim
85 of 85

  • The paper relies on this without support.

    Structures are simultaneously exposed to different EOCs.

    “Generally, structures are simultaneously exposed to different EOCs.”

  • The paper relies on this without support.

    Bridge systems with long spans are particularly susceptible to wind loads, and high winds can significantly affect these structures.

    “Bridge systems with long spans are particularly susceptible to wind loads, and as such, high winds can significantly affect these structures.”

  • The paper relies on this without support.

    During strong winds or typhoons, wind-induced vibrations contribute more energy to a system than damping, causing it to flutter or buffet.

    “During strong winds or typhoons, wind-induced vibrations contribute more energy to a system than damping, causing it to flutter or buffet.”

  • The paper relies on this without support.

    Effects of variations in humidity can be reflected in changes in boundary conditions and structural mass.

    “Effects of variations in humidity can be reflected in changes in boundary conditions and structural mass.”

  • The paper relies on this without support.

    As moisture content increases, structural frequencies tend to be reduced.

    “A slow process of changes occurs as the moisture content increases, and the structural frequencies tend to be reduced as a consequence.”

  • The paper relies on this without support.

    Historical data on which a static threshold is usually based might not cover the entire spectrum of EOC changes over the monitored period.

    “This is mainly because historical data, on which a static threshold is usually based, might not cover the entire spectrum of EOC changes over the monitored period.”

  • The paper relies on this without support.

    Effects of other EOC variations, such as traffic or wind loads, are unavoidable in SHM systems.

    “However, in actual practice, the effects of other EOC variations, such as traffic or wind loads, are unavoidable in SHM systems.”
7 of 7

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Boundary Element Method citation points to a perturbation-method reference

    Minor

    The text cites reference [80] for the Boundary Element Method, but reference 80 is titled as a perturbation technique. The Boundary Element Method reference is numbered 81.

  • Finite-difference table cites a spectral-method reference

    Minor

    Table 1 assigns reference [82] to finite-difference applications, but reference 82 is about the spectral finite element method.

2 of 2

Cited works that were retracted

Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.

  • Cites a retracted work: 10.1155/2021/2615029

    Info

    The cited work was retracted on 2023-12-29 (notice 10.1155/2023/9803564), not before this paper was published (2023-03-30).

    Cited work 10.1155/2021/2615029 · retracted 2023-12-29 · notice 10.1155/2023/9803564 · this paper published 2023-03-30

1 of 1

Cites

1 retracted

Flagged first, then by citations

12 of the 280 references couldn’t be matched to a known paper.

Research works printed but not linked: 13

  1. Hassani, S., Mousavi, M., and Sharif-Khodaei, Z. (2022). The Rise of Smart Cities, Elsevier.
  2. Sabatino, S., and Frangopol, D.M. (2016). Life-Cycle of Engineering Systems, CRC Press.
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Authors
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U.
Affiliations
  • University of Shahrood (IR)
  • UNSW Sydney (AU)
  • University of Technology Sydney (AU)
Corresponding author
Dackermann, Ulrike
ORCID
Venue
Buildings
Publisher
MDPI AG
Published
2023-03-30
Volume
13
Issue
4
Pages
918
ISSN
2075-5309 (ISSN-L)
DOI
10.3390/buildings13040918
OpenAlex
W4362465927
Cited by
96
References
280
Citations by year
2026: 22 · 2025: 51 · 2024: 18 · 2023: 5
FWCI
2.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Buildings (publishedVersion, cc-by)
  • OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
  • Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
Keywords
structural health monitoring, non-destructive testing, environmental and operational conditions, damage detection, sensor technology, signal processing, temperature effects, humidity, wind effects, vibration, mass loading, boundary conditions, damage sensitivity

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