Structural Health Monitoring of Large Civil Engineering Structures

Chen, Hua‐Peng; Ni, Yi‐Qing

doi:10.1002/9781119166641

Cited by 223Structural Health Monitoring TechniquesStructural Engineering and Materials AnalysisInfrastructure Maintenance and Monitoring

Structural Health Monitoring of Large Civil Engineering Structures

Chen, Hua‐Peng, Ni, Yi‐Qing·2018·Wiley eBooks

DOI 10.1002/9781119166641

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1002/9781119166641

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [140]

Sorted by citations

A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
Avcı, O., Abdeljaber, O., Serkan Kiranyaz, B., Hussein, M. F. M., Gabbouj, M., & Inman, D. J. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107077 · Cited by 1,463 · see citing works
“nvironmental and mechanical factors. The short term and long term damages cause the structures to age and shorten the design life which makes the monitoring process an important aspect for structures [1]. Monitoring structural damage on structures started with visual inspection and evolved in time with tremendous deve…”
Background
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review
Flah, M., Nunez, I., Chaabene, W. B., & Nehdi, M. L. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09471-9 · Cited by 540 · see citing works
“Causes of degradation of bridge structures include materials aging [49], corrosion of metals [137] and structural supports [140], mechanical overloading and other damage mechanisms [24].”
Background
Vibration‐based structural condition assessment using convolution neural networks
Khodabandehlou, H., Pekcan, G., & Fadali, M. S. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2308 · Cited by 211 · see citing works
Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review
Eltouny, K., Gomaa, M., & Liang, X. P. (2023) · Sensors · doi:10.3390/s23063290 · Cited by 163 · see citing works
Monitoring of transport infrastructure exposed to multiple hazards: a roadmap for building resilience
Achillopoulou, D. V., Mitoulis, S.-A., Argyroudis, S. A., & Wang, Y. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141001 · Cited by 133 · see citing works
Deep Neural Network and Evolved Optimization Algorithm for Damage Assessment in a Truss Bridge
Nguyen-Ngoc, L., Nguyen, Q. H., Roeck, G. D., Bui-Tien, T., & Wahab, M. M. A. (2024) · Mathematics · doi:10.3390/math12152300 · Cited by 111 · see citing works
Structural Health Monitoring and Management of Cultural Heritage Structures: A State-of-the-Art Review
Rossi, M., & Bournas, D. A. (2023) · Applied Sciences · doi:10.3390/app13116450 · Cited by 110 · see citing works
Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
“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].”
Background
Excitation and propagation of torsional T(0,1) mode for guided wave testing of pipeline integrity
Niu, X., Duan, W., Chen, H.-P., et al. (2018) · Measurement · doi:10.1016/j.measurement.2018.08.021 · Cited by 70 · see citing works
Self-citation
An In-Depth Survey Demystifying the Internet of Things (IoT) in the Construction Industry: Unfolding New Dimensions
Khurshid, K., Danish, A., Salim, M. U., Bayram, M., Ozbakkaloglu, T., & Mosaberpanah, M. A. (2023) · Sustainability · doi:10.3390/su15021275 · Cited by 73 · see citing works
“The complexities of IoT implementation in the industry analyzed in the present research will help the implementers”
Background
From traditional damage detection methods to Physics-Informed Machine Learning in bridges: A review
Mammeri, S., Barros, B., Conde, B., & Riveiro, B. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119862 · Cited by 65 · see citing works
Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review
Prakash, V., Debono, C. J., Musarat, M. A., Borg, R. P., Seychell, D., Ding, W., & Shu, J.-P. (2025) · Applied Sciences · doi:10.3390/app15094855 · Cited by 62 · see citing works
Real-time Bayesian damage identification enabled by sparse PCE-Kriging meta-modelling for continuous SHM of large-scale civil engineering structures
García‐Macías, E., & Ubertini, F. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105004 · Cited by 54 · see citing works
Review of robot-based automated measurement of vibration for civil engineering structures
Poorghasem, S., & Bao, Y.-N. (2023) · Measurement · doi:10.1016/j.measurement.2022.112382 · Cited by 53 · see citing works
Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
Biondi, F., Addabbo, P., Ullo, S. L., et al. (2020) · Remote Sensing · doi:10.3390/rs12233852 · Cited by 51 · see citing works
“The model of motion for the linear damped forced vibration of a structural dynamic system with a total number of M degrees of freedom can be expressed as [1]”
BackgroundMethodInfluential
Structural health monitoring of inland navigation structures and ports: a review on developments and challenges
Negi, P., Kromanis, R., Dorée, A. G., et al. (2023) · Structural Health Monitoring · doi:10.1177/14759217231170742 · Cited by 50 · see citing works
“145 The objective of OMA is to perform system identification, which includes identifying an unknown structural sys-tem that closely represents the actual structure.”
Modal frequency-environmental condition relation development using long-term structural health monitoring measurement: Uncertainty quantification, sparse feature selection and multivariate prediction
Mu, H.-Q., & Yuen, K.-V. (2018) · Measurement · doi:10.1016/j.measurement.2018.08.022 · Cited by 49 · see citing works
Time-Dependent Reliability of Aging Structures: Overview of Assessment Methods
Wang, C., Beer, M., & Ayyub, B. M. (2021) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0001176 · Cited by 48 · see citing works
A drive-by bridge inspection framework using non-parametric clusters over projected data manifolds
Cheema, P., Alamdari, M. M., Chang, K. C., Kim, C., & Sugiyama, M. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109401 · Cited by 47 · see citing works
The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)
Ghannadi, P., Kourehli, S. S., & Mirjalili, S. (2022) · Frattura ed Integrità Strutturale · doi:10.3221/igf-esis.62.32 · Cited by 45 · see citing works
Transmissibility function-based fault diagnosis methods for beam-like engineering structures: a review of theory and properties
Li, Q., Liao, M., & Jing, X. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06883-5 · Cited by 45 · see citing works
Structural health monitoring of timber buildings: a literature survey
Riggio, M., & Dilmaghani, M. (2019) · Building Research & Information · doi:10.1080/09613218.2019.1681253 · Cited by 43 · see citing works
Fatigue damage assessment of wind turbine composite blades using corrected blade element momentum theory
Zhang, C. C., Chen, H.-P., & Huang, T. (2018) · Measurement · doi:10.1016/j.measurement.2018.06.045 · Cited by 40 · see citing works
Self-citation
Unsupervised data-driven method for damage localization using guided waves
Lomazzi, L., Junges, R., Giglio, M., & Cadini, F. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.111038 · Cited by 38 · see citing works
Revolutionizing Structural Engineering: Applications of Machine Learning for Enhanced Performance and Safety
Chitkeshwar, A. K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10117-3 · Cited by 45 · see citing works
Reliability-Based Lifetime Fatigue Damage Assessment of Offshore Composite Wind Turbine Blades
Zhang, C. C., Chen, H.-P., Tee, K. F., et al. (2021) · Journal of Aerospace Engineering · doi:10.1061/(asce)as.1943-5525.0001260 · Cited by 37 · see citing works
Self-citation
Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations
Fernández-Navamuel, A., Pardo, D., Magalhães, F., Zamora-Sánchez, D., Omella, Á. J., & García-Sánchez, D. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110471 · Cited by 41 · see citing works
Multifractal analysis and wavelet leaders for structural damage detection of structures subjected to earthquake excitation
Pnevmatikos, N. G., Konstandakopoulou, F. D., Błachowski, B., et al. (2020) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2020.106328 · Cited by 36 · see citing works
Structural health monitoring of South America's first 6-Story experimental light-frame timber-building by using a low-cost RaspberryShake seismic instrumentation
Alarcón, M., Soto, P., Hernández, F., et al. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.115278 · Cited by 35 · see citing works
Design and Validation of a Low-Cost Structural Health Monitoring System for Dynamic Characterization of Structures
Caballero-Russi, D., Ortíz, A. R., Guzmán, A., et al. (2022) · Applied Sciences · doi:10.3390/app12062807 · Cited by 34 · see citing works
Reliability analysis and optimum maintenance of coastal flood defences using probabilistic deterioration modelling
Chen, H.-P., & Mehrabani, M. B. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.12.021 · Cited by 31 · see citing works
Self-citation
Development of laser-based displacement monitoring system and its application to large-scale spatial structures
Luo, Y., Chen, Y., Wan, H., et al. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00459-4 · Cited by 31 · see citing works
A two-stage framework for automated operational modal identification
Zeng, J., & Kim, Y. H. (2021) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2021.1919151 · Cited by 32 · see citing works
Integrated SHM Systems: Damage Detection Through Unsupervised Learning and Data Fusion
García‐Macías, E., & Ubertini, F. (2021) · Structural integrity · doi:10.1007/978-3-030-81716-9_12 · Cited by 31 · see citing works
Structural Fatigue Life Monitoring with Piezoelectric-Based Sensors: Fundamentals, Current Advances, and Future Directions
Ghaderiaram, A., Schlangen, E., & Fotouhi, M. (2025) · Sensors · doi:10.3390/s25020334 · Cited by 32 · see citing works
A state-of-the-art review of Bayesian finite element model updating techniques for structural systems
Kiran, R. P., Das, A., & Bansal, S. (2025) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2025.103761 · Cited by 31 · see citing works
Structural damage identification method of concrete dam based on multi-fidelity surrogate model collaboratively corrected by monitoring and simulation information
Guo, Y., Yuan, D., Wei, B., et al. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103559 · Cited by 28 · see citing works
Lifetime Performance Assessment of Railway Ballastless Track Systems Affected by a Mortar Interface Defect
Feng, Q., Sun, K., Chen, H.-P., et al. (2019) · Journal of Aerospace Engineering · doi:10.1061/(asce)as.1943-5525.0001017 · Cited by 28 · see citing works
Self-citation
Identification of Structural Stiffness and Mass using Bayesian Model Updating Approach with Known Added Mass: Numerical Investigation
Zeng, J., & Kim, Y. H. (2020) · International Journal of Structural Stability and Dynamics · doi:10.1142/s0219455420501230 · Cited by 26 · see citing works
Design and Implementation of a Connection between Augmented Reality and Sensors
Agüero, M., Maharjan, D., Rodriguez, M. D. P., Mascareñas, D., & Moreu, F. (2020) · Robotics · doi:10.3390/robotics9010003 · Cited by 27 · see citing works
Experimental comparison of automatic operational modal analysis algorithms for application to long-span road bridges
Dederichs, A. C., & Øiseth, O. A. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110485 · Cited by 26 · see citing works
Measuring Reference-Free Total Displacements of Piles and Columns Using Low-Cost, Battery-Powered, Efficient Wireless Intelligent Sensors (LEWIS2)
Agüero, M., Ozdagli, A. I., & Moreu, F. (2019) · Sensors · doi:10.3390/s19071549 · Cited by 26 · see citing works
“Third, WS-based SHM systems implement real-time and autonomous monitoring [4,30,31].”
Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
Hoxha, E., Vidal, Y., & Pozo, F. (2020) · Applied Sciences · doi:10.3390/app10196972 · Cited by 25 · see citing works
“However, the fractal dimension of a regular time series normally lies within the range [1, 2].”
Background
Measurement platform for structural health monitoring application of large scale structures
Lambinet, F., & Khodaei, Z. S. (2022) · Measurement · doi:10.1016/j.measurement.2021.110675 · Cited by 25 · see citing works
Optical Fiber Sensors Systems in Oil Tanks: Toward Structural Health Monitoring and Liquid-Level Estimation
Lazaro, R. C., de Souza, E. R., Frizera, A., et al. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3261985 · Cited by 25 · see citing works
Monitoring of Critical Infrastructures by Micromotion Estimation: The Mosul Dam Destabilization
Biondi, F., Addabbo, P., Clemente, C., Ullo, S. L., & Orlando, D. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3030977 · Cited by 26 · see citing works
“The dynamic stiffness Z(ω) can be defined as the inverse of the frequency response [18] Z(ω) = H(ω)−1 = (−ω2M+ jωC+K) .”
BackgroundInfluential
Thermal deformation monitoring of a highway bridge: combined analysis of geodetic and satellite-based InSAR measurements with structural simulations
Lorenz, R., Petryna, Y. S., Lubitz, C., et al. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00779-9 · Cited by 24 · see citing works
Bearing assessment tool for longitudinal bridge performance
García-Sánchez, D., Fernández-Navamuel, A., Zamora-Sánchez, D., Alvear, D., & Pardo, D. (2020) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00432-1 · Cited by 22 · see citing works
“Over the last decades, monitoring systems have gained importance in our society [1,2].”
Background
Analysis of Acoustic Emission Signals Processed with Wavelet Transform for Structural Damage Detection in Concrete Beams
Machorro-López, J. M., Hernandez-Figueroa, J. A., Carrion-Viramontes, F. J., et al. (2023) · Mathematics · doi:10.3390/math11030719 · Cited by 23 · see citing works
Piezoelectric pressure sensors based on GO-modified P(VDF-TrFE) fibers for vacuum applications
Shahzad, A., Chen, Z., Haidry, A. A., Mehmood, A., & Khan, Z. M. (2020) · Journal of Materials Science Materials in Electronics · doi:10.1007/s10854-020-04405-4 · Cited by 23 · see citing works
Assessment of railway bridge pier settlement based on train acceleration response using machine learning algorithms
Abdu, D. M., Wei, G., & Wang, Y. (2023) · Structures · doi:10.1016/j.istruc.2023.03.167 · Cited by 22 · see citing works
Structural Health Monitoring of Bridges under the Influence of Natural Environmental Factors and Geomatic Technologies: A Literature Review and Bibliometric Analysis
Rădulescu, M., Rădulescu, G. M. T., Naș, S. M., Rădulescu, A. T., & Rădulescu, C. (2024) · Buildings · doi:10.3390/buildings14092811 · Cited by 21 · see citing works
Cointegration strategy for damage assessment of offshore platforms subject to wind and wave forces
Kuai, H., Civera, M., Coletta, G., et al. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117692 · Cited by 21 · see citing works
Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategies
Adebiyi, T. A., Ajenifuja, N. A., & Zhang, R. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4066181 · Cited by 22 · see citing works
“Local monitoring involves the evaluation of specific parameters in the structure, which is usually employed for non-complex and modern constructions [37,43,133–135].”
Background
A Framework for Long-Term Vibration-Based Monitoring of Bridges
Erduran, E., Ulla, F. K., & Næss, L. (2021) · Sensors · doi:10.3390/s21144739 · Cited by 20 · see citing works
DFOS measurements for strain analysis of anchorage zone in 57‐year‐old posttensioned precast girder using static and high‐frequency approach
Derkowski, W., Sieńko, R., Walczak, R., Howiacki, T., & Bednarski, Ł. (2021) · Structural Concrete · doi:10.1002/suco.202100002 · Cited by 19 · see citing works
Monitoring Strategic Hydraulic Infrastructures by Brillouin Distributed Fiber Optic Sensors
Bertulessi, M., Bignami, D. F., Boschini, I., et al. (2022) · Water · doi:10.3390/w14020188 · Cited by 19 · see citing works
Iterative hierarchical clustering algorithm for automated operational modal analysis
Romanazzi, A., Scocciolini, D., Savoia, M., & Buratti, N. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105137 · Cited by 20 · see citing works
Ensemble learning-based structural health monitoring of a bridge using an interferometric radar system
Yaghoubzadehfard, A., Lumantarna, E., Herath, N., et al. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00789-7 · Cited by 18 · see citing works
“The main drawback revealed by real-world cases is the difficulty in achieving GPS positioning accuracy [9].”
A New Benchmark Problem for Structural Damage Detection: Bolt Loosening Tests on a Large-Scale Laboratory Structure
Avcı, O., Abdeljaber, O., Kıranyaz, S., et al. (2021) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-030-77143-0_2 · Cited by 16 · see citing works
Challenges in applying vibration-based damage detection to highway bridge structures
Tefera, B., Zekaria, A., & Gebre, A. (2023) · Asian Journal of Civil Engineering · doi:10.1007/s42107-023-00594-5 · Cited by 16 · see citing works
Review on quick safety assessment of building structures in complex urban environment after extreme explosion events
Shi, Y., Liu, S., Li, Z., & Ding, Y. (2022) · International Journal of Protective Structures · doi:10.1177/20414196221104146 · Cited by 16 · see citing works
“…damage identification: (1) emergence of the damage, (2) damage localization, (3) damage type (e.g. cracks, steel yielding and concrete spalling), (4) severity or extent of the damage and (5) structural prognosis to predict the remaining useful life (Chen and Ni, 2018; Tadeusz and Staszewski, 2013).”
Background
Model Updating Concept Using Bridge Weigh-in-Motion Data
Hekič, D., Anžlin, A., Kreslin, M., et al. (2023) · Sensors · doi:10.3390/s23042067 · Cited by 15 · see citing works
Evaluating bolt connecting faults in ring-type structures with nonlinear fault-induced loads and transmissibility functions
Li, Q., Wang, T., Liao, M., et al. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110212 · Cited by 15 · see citing works
A review on fatigue monitoring of structures
García-Fernández, N., Aenlle, M. L., Álvarez‐Vázquez, A., Muñiz‐Calvente, M., & Pelayo, F. (2023) · International Journal of Structural Integrity · doi:10.1108/ijsi-09-2022-0117 · Cited by 17 · see citing works
A novel fault evaluation method based on nonlinear vibration features and Euclidean distance measurement for grid-like structures
Li, Q., Ma, R., Liao, M., et al. (2023) · Structural Health Monitoring · doi:10.1177/14759217231163085 · Cited by 15 · see citing works
“2,21 As shown in Figure 2, the T-type MDOF model has four masses moving in the vertical direction, and three surrounding masses are connected to the central mass with damper-spring components.”
Background
Visualization of real-time displacement time history superimposed with dynamic experiments using wireless smart sensors and augmented reality
Agüero, M., Doyle, D., Mascareñas, D., et al. (2023) · Earthquake Engineering and Engineering Vibration · doi:10.1007/s11803-023-2184-x · Cited by 15 · see citing works
Structural Health Monitoring Using Synchrosqueezed Wavelet Transform on IASC-ASCE Benchmark Phase I
Sanchez, W. D., de Brito, J. V., & Ávila, S. M. (2020) · International Journal of Structural Stability and Dynamics · doi:10.1142/s0219455420501382 · Cited by 15 · see citing works
Probabilistic parameter estimation and reliability assessment of a simulated sheet pile wall system
Chai, X., Rózsás, Á., Slobbe, A., et al. (2021) · Computers and Geotechnics · doi:10.1016/j.compgeo.2021.104567 · Cited by 15 · see citing works
Numerical Analysis and Experimental Verification of Damage Identification Metrics for Smart Beam with MFC Elements to Support Structural Health Monitoring
Koszewnik, A. P., Lesniewski, K., & Pakrashi, V. (2021) · Sensors · doi:10.3390/s21206796 · Cited by 14 · see citing works
Damage assessment of suspension footbridge using vibration measurement data combined with a hybrid bee-genetic algorithm
Ngoc-Nguyen, L., Ngoc-Tran, H., Khatir, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-24445-6 · Cited by 14 · see citing works
Channel mode attention network for structural damage identification
Wang, Y., Song, X., Li, R., Yang, F., Xiao, Y., Zheng, S., Wang, K., & Li, X. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.119389 · Cited by 15 · see citing works
A summary evaluation of output-only damage-sensitive features for structural health monitoring of offshore platforms subjected to ambient loads
Rahgozar, R., & Bitaraf, M. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.112892 · Cited by 13 · see citing works
RETRACTED: Synthetic Aperture Radar Doppler Tomography Reveals Details of Undiscovered High-Resolution Internal Structure of the Great Pyramid of Giza
Biondi, F., & Malanga, C. (2022) · Remote Sensing · doi:10.3390/rs14205231 · Cited by 12 · see citing works
“The procedure described above was made available to perform a comprehensive survey of large road bridges, according to [55].”
Method
Retracted
Bayesian system identification for structures considering spatial and temporal correlation
Koune, I., Rózsás, Á., Slobbe, A., et al. (2023) · Data-Centric Engineering · doi:10.1017/dce.2023.18 · Cited by 12 · see citing works
Vibration-based SHM of Dębica railway steel bridge with optimized ANN and ANFIS
Nguyen, D. C., Salamak, M., Katunin, A., et al. (2024) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2024.108505 · Cited by 12 · see citing works
Wireless Printed Large‐Area Sensors for Continuous Structural Health Monitoring
Hassan, M. F., Li, Z., Islam, M. S., et al. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202401782 · Cited by 12 · see citing works
“[18,19] Additionally, covering large infrastructure with conventional strain gauges is challenging due to the extensive number of sensors required and the associated installation and monitoring complexity.”
Displacement Study of a Large-Scale Freeform Timber Plate Structure Using a Total Station and a Terrestrial Laser Scanner
Nguyen, A. C., & Weinand, Y. (2020) · Sensors · doi:10.3390/s20020413 · Cited by 12 · see citing works
Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups
Das, A., & Debnath, N. (2021) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0001135 · Cited by 12 · see citing works
A systematic review of structural materials health monitoring system for girder-type bridges
Al-Nasar, M. K. R., & Al-Zwainy, F. M. S. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.12.385 · Cited by 12 · see citing works
A new damage indicator based on homogeneity and wireless accelerometers for evaluating the structural condition of a cable-stayed bridge
Valtierra‐Rodriguez, M., Machorro-López, J. M., Amézquita-Sánchez, J. P., et al. (2023) · Developments in the Built Environment · doi:10.1016/j.dibe.2023.100166 · Cited by 11 · see citing works
Attention-based image captioning for structural health assessment of apartment buildings
Dinh, N. N. H., Shin, H., Ahn, Y., Oo, B. L., & Lim, B. T. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105677 · Cited by 11 · see citing works
Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network
Mansouri, T. S., Lubarsky, G. V., Finlay, D., et al. (2024) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan13060079 · Cited by 11 · see citing works
A step excitation optimization method for intensive modal identification of cable net structures
Wang, X., & Deng, H. (2019) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2019.115017 · Cited by 11 · see citing works
Quantitative Assessment of Nonstationarity of Wind Speed Signal Using Recurrence Plot
Xu, Z., Wang, H., Wan, H., & Li, H. (2019) · Journal of Aerospace Engineering · doi:10.1061/(asce)as.1943-5525.0001092 · Cited by 11 · see citing works
Vision-Based Digital Shadowing to Reveal Hidden Structural Dynamics of a Real Supertall Building
Gu, D., Yue, Q., Li, L., et al. (2024) · Engineering · doi:10.1016/j.eng.2024.10.002 · Cited by 10 · see citing works
Smart Concrete Using Optical Sensors Based on Bragg Gratings Embedded in a Cementitious Mixture: Cure Monitoring and Beam Test
Souza, E., Pinheiro, P., Coutinho, F., Dias, J. V. F., Pilar, R., Pontes, M. J., & Junior, A. G. L. (2024) · Sensors · doi:10.3390/s24247998 · Cited by 11 · see citing works
“…is often used in large buildings and consists of an embedded steel reinforcement; in this case, the best sensor position for the monitoring of a structure manufactured with reinforcing concrete is precisely on the steel reinforcing, in other words, with the sensors embedded in the concrete [16].”
How can sensors be used for sustainability improvement?
Kot, P., Hashim, K., Muradov, M., & Alkhaddar, R. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823987-2.00011-8 · Cited by 9 · see citing works
Fatigue damage assessment of a large rail-cum-road steel truss-arch bridge using structural health monitoring dynamic data
Chen, H.-P., Lu, S., Wu, W., et al. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e03772 · Cited by 9 · see citing works
Self-citation
Wind field characterization with skip-connected variational autoencoder for data cleaning under deck disturbance effects
Chen, N., Liu, G., Ma, R., et al. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119905 · Cited by 9 · see citing works
SENSING RAIL SYSTEM WITH PIEZOELECTRIC ELEMENTS
Machů, Z., Kšica, F., Hadaš, Z., et al. (2021) · MM Science Journal · doi:10.17973/mmsj.2021_03_2020066 · Cited by 9 · see citing works
“SHM is a very current topic in many other engineering fields such as mechanical engineering [Salameh 2018], civil infrastructure [Chen 2018], aviation industry [Qing 2019] and others.”
Background
Bridge Condition Assessment Using Supervised Decision Trees
Bianchi, S., & Biondini, F. (2021) · Lecture notes in civil engineering · doi:10.1007/978-3-030-91877-4_126 · Cited by 9 · see citing works
Performance assessment of steel cantilever beams based on connection behaviour using DIC technique and improved hybrid PSO algorithm
Khaloo, A., & Amirahmadi, A. (2023) · Structural Health Monitoring · doi:10.1177/14759217231163257 · Cited by 9 · see citing works
“30 In this method, the error between the experimental data and the initial model is determined using an objective function, and then the function is minimized by different optimization methods to obtain the updated model with the least error, in a way that the optimal value of the finite element…”
Method
Real-Time Vision-Based Dynamic Monitoring of Vertical and Horizontal Transverse Displacements in Bridge Decks
Micozzi, F., Morici, M., Zona, A., et al. (2025) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-7597 · Cited by 8 · see citing works
Enhancing structural health monitoring data management and damage detection in digital twins with blockchain and smart contracts
Mariniello, G., Gragnaniello, C., & Asprone, D. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106558 · Cited by 9 · see citing works
Integration of locating baseline-free nonlinear elements and identifying model-free nonlinear restoring forces in structures
Lei, Y., Yang, X., Mi, J., et al. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-08536-1 · Cited by 8 · see citing works
Structural characterization of a suspension bridge by mapping the temperature effects on strain response based on neural network models
Miranda, F. N., Mata, J., Santos, J. P., & Romão, X. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00855-0 · Cited by 7 · see citing works
Damage Estimation of a Concrete Pier When Exposed to Extreme Flood and Debris Loading
Nasim, M., & Setunge, S. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10050710 · Cited by 7 · see citing works
Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
Bertagnoli, G., Ferrara, M., Lucà, F., et al. (2023) · Applied Sciences · doi:10.3390/app13031477 · Cited by 7 · see citing works
A Practical Review of Prairie Dog Optimization Algorithm in Solving Damage Identification Problems in Engineering Structures
Ngoc-Nguyen, L., Hoa-Tran, Khatir, S., et al. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-24041-6_24 · Cited by 7 · see citing works

Authors: Chen, H., & Ni, Y.
Affiliations: University of Greenwich (GB)
Hong Kong Polytechnic University (HK)
ORCID: Ni, Yi‐Qing
Venue: Wiley eBooks
Publisher: Wiley
Published: 2018-02-02
DOI: 10.1002/9781119166641
OpenAlex: W2792411901
Cited by: 223
References: 0
Citations by year: 2026: 9 · 2025: 29 · 2024: 41 · 2023: 43 · 2022: 22 · 2021: 37 · 2020: 23 · 2019: 11 · 2018: 7 · 2017: 1
FWCI: 31.47
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Wiley eBooks (publishedVersion)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords: structural health monitoring, infrastructure monitoring, structural assessment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Structural Health Monitoring of Large Civil Engineering Structures

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cited by

Related

Record

About these checks