Data interpretation and forecasting of SHM heteroscedastic measurements under typhoon conditions enabled by an enhanced Hierarchical sparse Bayesian Learning model with high robustness

Gaussian processes for time-series modelling

Roberts, S., Osborne, M. A., Ebden, M., Reece, S., Gibson, N. P., & Aigrain, S. (2012) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2011.0550 · Cited by 608 · see citing works

Convolutional neural network–based data recovery method for structural health monitoring

Oh, B. K., Glišić, B., Kim, Y., & Park, H. S. (2020) · Structural Health Monitoring · doi:10.1177/1475921719897571 · Cited by 151 · see citing works

Structural damage detection method using frequency response functions

Bandara, R. P., Chan, T. H. T., & THAMBIRATNAM, D. P. (2014) · Structural Health Monitoring · doi:10.1177/1475921714522847 · Cited by 150 · see citing works

Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

Zhang, Y., Wang, H., Wan, H., Mao, J., & Xu, Y. (2020) · Structural Health Monitoring · doi:10.1177/1475921720977020 · Cited by 105 · see citing works

Displacement Model for Concrete Dam Safety Monitoring via Gaussian Process Regression Considering Extreme Air Temperature

Kang, F., & Li, J. (2019) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002467 · Cited by 88 · see citing works

Gaussian Process Regression Stochastic Volatility Model for Financial Time Series

Han, J., Zhang, X.-P. S., & Wang, F. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2570738 · Cited by 84 · see citing works

Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring

Huang, Y., Beck, J. L., & Li, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12408 · Cited by 79 · see citing works

A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring

Ni, Y., & Zhang, Q.-H. (2020) · Structural Health Monitoring · doi:10.1177/1475921720921772 · Cited by 64 · see citing works

Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme

Wang, Q., Dai, Y., Ma, Z., Ni, Y., Tang, J., Xu, X., & Wu, Z. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3070 · Cited by 58 · see citing works

Guided-wave signal processing by the sparse Bayesian learning approach employing Gabor pulse model

Wu, B., Huang, Y., Chen, X., Krishnaswamy, S., & Li, H. (2016) · Structural Health Monitoring · doi:10.1177/1475921716665252 · Cited by 54 · see citing works

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

Wang, Q., Dai, Y., Ma, Z., Wang, J., Lin, J., Ni, Y., Ren, W., Jiang, J. D., Yang, X., & Yan, J.-R. (2023) · Structural Health Monitoring · doi:10.1177/14759217231170316 · Cited by 54 · see citing works

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

Wang, Q., Wang, C.-B., Ma, Z., Chen, W., Ni, Y., Wang, C., Yan, B.-G., & Guan, P.-X. (2022) · Structural Health Monitoring · doi:10.1177/14759217221079529 · Cited by 52 · see citing works

Unsupervised Diagnostic and Monitoring of Defects Using Waveguide Imaging With Adaptive Sparse Representation

Gao, B., Woo, W. L., Tian, G. Y., & Zhang, H. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2492924 · Cited by 45 · see citing works

Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis

Jamali, S., Chan, T. H. T., Nguyễn, A., & THAMBIRATNAM, D. P. (2018) · Structural Health Monitoring · doi:10.1177/1475921718808462 · Cited by 43 · see citing works

A Gaussian process–based approach to cope with uncertainty in structural health monitoring

Teimouri, H., Milani, A. S., Loeppky, J. L., & Seethaler, R. (2016) · Structural Health Monitoring · doi:10.1177/1475921716669722 · Cited by 42 · see citing works

Towards long‐transmission‐distance and semi‐active wireless strain sensing enabled by dual‐interrogation‐mode RFID technology

Wang, Q., Zhang, C., Ma, Z., Jiao, G., Jiang, X., Ni, Y., Wang, Y., Du, Y., Qu, G., & Huang, J. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3069 · Cited by 39 · see citing works

A Bayesian approach for sparse flaw detection from noisy signals for ultrasonic NDT

Wu, B., Huang, Y., & Krishnaswamy, S. (2016) · NDT & E International · doi:10.1016/j.ndteint.2016.10.005 · Cited by 32 · see citing works

SHM deformation monitoring for high-speed rail track slabs and Bayesian change point detection for the measurements

Wang, Q., Zhang, C., Ma, Z.-G., Huang, J., Ni, Y., & Zhang, C. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.124337 · Cited by 32 · see citing works

Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

Ye, X., Ding, Y., & Wan, H. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2650 · Cited by 27 · see citing works

Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring

Sun, J., Yu, Y., & Wen, J. (2017) · Sensors · doi:10.3390/s17061454 · Cited by 24 · see citing works

Dynamic reliability prediction for the steel box girder based on multivariate Bayesian dynamic Gaussian copula model and SHM extreme stress data

Liu, Y. F., & Fan, X. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2531 · Cited by 20 · see citing works

Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring

Pang, L., Liu, J., Harkin, J., Martin, G., McElholm, M., Javed, A., & Mcdaid, L. J. (2020) · Sensors · doi:10.3390/s20185126 · Cited by 20 · see citing works

Application of artificial neural network to predict dynamic displacements from measured strains for a highway bridge under traffic loads

Moon, H. S., Hwang, Y. K., Kim, M. K., Kang, H.-T., & Lim, Y. M. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-021-00531-7 · Cited by 20 · see citing works

Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing

Wang, S., Gao, J., Lin, C., Li, H., & Huang, Y. (2022) · International Journal of Rail Transportation · doi:10.1080/23248378.2022.2075944 · Cited by 20 · see citing works

Estimation of structural response using convolutional neural network: application to the Suramadu bridge

Pamuncak, A. P., Salami, M. R., Adha, A., Budiono, B., & Laory, I. (2021) · Engineering Computations · doi:10.1108/ec-12-2020-0695 · Cited by 14 · see citing works

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Bai, H., Wen, L., Ma, Y., & Jia, X. (2022) · Sensors · doi:10.3390/s22103884 · Cited by 14 · see citing works

Sparse Bayesian learning for damage identification using nonlinear models: Application to weld fractures of steel‐frame buildings

Filippitzis, F., Kohler, M. D., Heaton, T. H., & Beck, J. L. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2870 · Cited by 13 · see citing works

SHM-based probabilistic representation of wind properties: Bayesian inference and model optimization

Ye, X. W., Li, Y., Xi, P.-P., & Liu, H. (2018) · Smart Structures and Systems · doi:10.12989/sss.2018.21.5.601 · Cited by 9 · see citing works

Assessment of Structural Reliability of Bridge Beams Based on Measured Symptoms

Quattrone, A., Matta, E., Fragonara, L. Z., Ceravolo, R., & Stefano, A. D. (2013) · Key engineering materials · doi:10.4028/www.scientific.net/kem.569-570.358 · Cited by 2 · see citing works

Multiclass Data Classification Using Multinomial Logistic Gaussian Process Model

Cho, W., Park, S., & Kim, S.-K. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-7605-3_21 · Cited by 1 · see citing works

Bayesian in-situ parameter estimation of metallic plates using piezoelectric transducers

Asadi, S., Shamshirsaz, M., & Vaghasloo, Y. A. (2020) · Smart Structures and Systems · doi:10.12989/sss.2020.26.6.735 · Cited by 1 · see citing works

Application of Bayesian networks in reliability assessment: A systematic literature review

Wang, Q., Jin, C., Ni, Y., Xiao, Y., Liu, N., Liu, S., & Feng, W. (2024) · Structures · doi:10.1016/j.istruc.2024.108098 · Cited by 59 · see citing works

Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review

Wang, Q., Lu, A.-W., Ni, Y., Wang, J., & Ma, Z. (2025) · Sensors · doi:10.3390/s25123577 · Cited by 42 · see citing works

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Ran, S.-C., Wang, Q., Wang, J., Ni, Y., Guo, Z.-X., & Luo, Y. (2024) · Applied Sciences · doi:10.3390/app14083213 · Cited by 37 · see citing works

Physics-Informed Neural Networks for the Structural Analysis and Monitoring of Railway Bridges: A Systematic Review

Martinez, Y., Rojas, L., Peña, Á., Valenzuela, M. A., & García, J. (2025) · Mathematics · doi:10.3390/math13101571 · Cited by 33 · see citing works

Data-driven wind-induced response prediction for slender civil infrastructure: Progress, challenges and opportunities

Zhang, Y., Li, H., & Wang, H. (2025) · Structures · doi:10.1016/j.istruc.2025.108650 · Cited by 21 · see citing works

A Concise Review of State-of-the-Art Sensing Technologies for Bridge Structural Health Monitoring

Kang, X., Zhu, B., Cai, Y., Xiao, Y., Liu, N., Guo, Z.-X., Wang, Q., & Luo, Y. (2025) · Sensors · doi:10.3390/s25175460 · Cited by 20 · see citing works

Design and Optimization of a Hybrid Railcar Structure with Multilayer Composite Panels

Cascino, A., Meli, E., & Rindi, A. (2025) · Materials · doi:10.3390/ma18215013 · Cited by 16 · see citing works

The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice

Radoń, U., & Zabojszcza, P. (2025) · Applied Sciences · doi:10.3390/app15010342 · Cited by 15 · see citing works

Adaptive forecasting of stochastic crack growth using empirical mode decomposition: Gaussian process regression for structural health monitoring

Jiang, S., Wang, Y., Cheng, Z., & Chen, B. (2026) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2026.2617965 · Cited by 14 · see citing works

A Study on the Preference of Electric Vehicle Front Design Elements

Sun, Z., & Park, K. (2024) · Applied Sciences · doi:10.3390/app14083262 · Cited by 11 · see citing works

A Bayesian Probabilistic Framework for Building Models for Structural Health Monitoring of Structures Subject to Environmental Variability

Simon, P., Schneider, R., Baeßler, M., & Morgenthal, G. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/4204316 · Cited by 9 · see citing works

Deep learning for anomaly detection in traditional Tibetan timber structures: A VAE-based model with multi-head attention and LSTM encoding and state-based thresholding

Qian, X., & Yang, N. (2025) · Structures · doi:10.1016/j.istruc.2025.109092 · Cited by 7 · see citing works

A Deep Learning PM2.5 Hybrid Prediction Model Based on Clustering–Secondary Decomposition Strategy

Zeng, T., Liu, R., Liu, Y., Shi, J., Luo, T., Xi, Y., Zhao, S., Chen, C., Pan, G., Zhou, Y., et al. (2024) · Electronics · doi:10.3390/electronics13214242 · Cited by 6 · see citing works

Multistage Static and Dynamic Optimization Framework for Composite Laminates in Lightweight Urban Rail Vehicle Car Bodies

Cascino, A., Distaso, F., Meli, E., & Rindi, A. (2026) · Materials · doi:10.3390/ma19030531 · Cited by 5 · see citing works

Probabilistic Interference Prediction for Dynamic 6G In-X Sub-Networks

Gautam, P., Bockelmann, C., & Dekorsy, A. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3554993 · Cited by 5 · see citing works

Towards Sustainable Airport Operations: Emission Analysis of Taxiing Solutions

Maciejewska, M., & Kurzawska-Pietrowicz, P. (2025) · Sustainability · doi:10.3390/su17188242 · Cited by 5 · see citing works

Unsupervised learning-based data cleaning framework for structural health monitoring using GoogLeNet and clustering models

Deng, Y., Zhao, Y., He, Y., Ju, H., & Li, A. (2025) · Structural Health Monitoring · doi:10.1177/14759217251348394 · Cited by 4 · see citing works

Proactive Cable Replacement Planning Using Neural Weibull Proportional Hazard Modeling and Bayesian Nested Monte Carlo Simulation Under Data Deficiencies

Shadi, M. R., Mortensen, L. K., Mirshekali, H., & Shaker, H. R. (2025) · IEEE Access · doi:10.1109/access.2025.3613832 · Cited by 4 · see citing works

Key Technologies for Intelligent Construction and Smart Operation and Maintenance of Large-Scale Hydraulic Tunnels

Chen, Y., Lin, P., Zhu, G., Yan, X. Q., Wang, Z., Mou, R., Deng, Z., Xie, H., Cai, M., & Wang, H. (2025) · Journal of Intelligent Construction · doi:10.26599/jic.2025.9180102 · Cited by 4 · see citing works

State-of-the-Art Technologies and Emerging Trends in Radar-Based Vital Signs Sensing: A Review

Yuan, T., Kuo, Y., Ren, A., Huo, R., Sun, Q., & Zahid, A. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3646149 · Cited by 4 · see citing works

Robotics and unmanned aerial vehicles for smart structural health monitoring: A systematic review

Du, W., Bao, X., Zhang, X., Qin, J., & Broere, W. (2026) · Structural Health Monitoring · doi:10.1177/14759217261455417 · Cited by 5 · see citing works

Leak Event Diagnosis for Power Plants: Generative Anomaly Detection Using Prototypical Networks

Jeong, J., Yeo, D., Roh, S., Jo, Y., & Kim, M. S. (2024) · Sensors · doi:10.3390/s24154991 · Cited by 4 · see citing works

Re-calibrating measurements of low-cost air quality monitors using PCR-GPR air quality forecasting models

Liu, B., Yang, S., & Wang, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0314417 · Cited by 4 · see citing works

On the Effect of Intra- and Inter-Node Sampling Variability on Operational Modal Parameters in a Digital MEMS-Based Accelerometer Sensor Network for SHM: A Preliminary Numerical Investigation

Brambilla, M., Chiariotti, P., & Cigada, A. (2025) · Sensors · doi:10.3390/s25165044 · Cited by 3 · see citing works

The optimization and load forecasting of power distribution units in data halls

Abaddi, S. (2026) · Applied Operations and Analytics · doi:10.1080/29966892.2026.2649444 · Cited by 3 · see citing works

Design and Uncertainty Evaluation of a Calibration Setup for Turbine Blades Vibration Measurement

Capponi, L., Tribbiani, G., Medici, V., Fabri, S., Prato, A., Castellini, P., Schiavi, A., Paone, N., & Rossi, G. L. (2024) · Sensors · doi:10.3390/s24248050 · Cited by 3 · see citing works

Integrating WSN and IoT for enhanced structural health monitoring in real-time using neural networks: a novel approach

Mahdi, S. Q., Gharghan, S. K., Al-Baghdadi, H. A., & Mutlag, A. H. (2025) · Asian Journal of Civil Engineering · doi:10.1007/s42107-025-01459-9 · Cited by 3 · see citing works

Bayesian system identification considering the spatial correlation of distributed loads

Li, Y., Sun, H., Sun, L., Liu, J., Luo, L., & Ying Lei, A. (2025) · Structural Health Monitoring · doi:10.1177/14759217251357356 · Cited by 2 · see citing works

Bayesian sequential multi-measurement updating for interference-resistant bridge influence line identification

Zhao, L., Chen, Z., & Yan, W. (2025) · Measurement · doi:10.1016/j.measurement.2025.119083 · Cited by 2 · see citing works

Hierarchical Bayesian model for time-varying parameter inversion and uncertainty quantification in silted bank slope-piled wharf system

Xiong, Q., Liu, H., Cui, C., Zhang, P., Ji, Z., & You, Z.-J. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.123229 · Cited by 2 · see citing works

Hypersector-Based Method for Real-Time Classification of Wind Turbine Blade Defects

Дубчак, Л., Rusyn, B., Wolff, C., Ciszewski, T., Sachenko, A., & Bodyanskiy, Y. V. (2026) · Energies · doi:10.3390/en19020442 · Cited by 2 · see citing works

Optimal Design of CFRP Railway Carbody Laminates via Multi-Criterion Fiber Orientation Sensitivity

Cascino, A., Meli, E., & Rindi, A. (2026) · Materials · doi:10.3390/ma19071355 · Cited by 2 · see citing works

Physics-Informed Neural Network Framework for Predicting Creep-Induced Camber in Simply Supported Prestressed Concrete Girder Bridges

Zhu, L. X., Gao, L., Zhang, L., Wang, B., Du, W., & Zhang, M. (2026) · Buildings · doi:10.3390/buildings16071380 · Cited by 2 · see citing works

Dynamic Identification of the Sarcophagus of the Spouses by Means of Digital Video Analysis

Fioriti, V. A., Occhipinti, G., Roselli, I., Cataldo, A., Clemente, P., Colucci, A., AlShawa, O., & Sorrentino, L. (2025) · Heritage · doi:10.3390/heritage8040133 · Cited by 1 · see citing works

MBAV: A Positional Encoding-Based Lightweight Network for Detecting Embedded Parts in Prefabricated Composite Slabs

Yu, F., Yuan, L., Jin, Q., & Hu, D. (2025) · Buildings · doi:10.3390/buildings15162850 · Cited by 1 · see citing works

Hierarchical Bayesian Intelligence Framework for Uncertainty Quantification and Reliability Assessment of Solid Oxide Fuel Cells

Park, E.-J., Cheon, Y.-J., & Lee, J. (2025) · IEEE Access · doi:10.1109/access.2025.3626137 · Cited by 1 · see citing works

Stiffness Separation Method for Damage Identification in Continuous Rigid Frame Bridges

Xiao, F., Xu, L., Yan, Y., & Xiang, Y. (2025) · Sensors · doi:10.3390/s25237141 · Cited by 1 · see citing works

A Dynamic Dimension Reduction Method for Structural Health Monitoring Data Based on Sparse Bayesian Inference

Zeng, H., & Zhu, Y. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/5560897 · Cited by 1 · see citing works

Research on intelligent classification method of highway tunnel surrounding rock classification based on parameters while drilling

Lin, Z., Wu, Y., Wang, J., He, H., Yuan, L., Chen, G., & Han, S. (2025) · Open Geosciences · doi:10.1515/geo-2025-0822 · Cited by 1 · see citing works

Stochastic–Fuzzy Assessment Framework for Firefighting Functionality of Urban Water Distribution Networks Against Post-Earthquake Fires

He, X., Huang, H., Xu, F., Zhang, C., & Qin, T. (2026) · Sustainability · doi:10.3390/su18020949 · Cited by 1 · see citing works

An RFID Tag Sensor for Bolt Preload Monitoring

Zhang, J., Ou, Y., Zhu, D., Liu, D., Gao, B., & Ding, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3662406 · Cited by 1 · see citing works

A study on aged and cured behaviour of carbon based self-sensing concrete sensors for structural health monitoring purposes

Siahkouhi, M., Rashidi, M., MASHIRI, F. R., Aslani, F., & Shrestha, K. (2026) · Measurement · doi:10.1016/j.measurement.2026.121518 · Cited by 1 · see citing works

Bayesian Inference and Gaussian Process Regression for Structural Health Monitoring of Box Girder Bridges Joints

Sinsamutpadung, N., Kamchoom‬, V., & Juntim, G. (2025) · International Journal of Structural Stability and Dynamics · doi:10.1142/s0219455426503074

Identification of Static Loads in Wharf Mooring Cables Using the Influence Coefficient Method

Zhou, J., Xiao, C., Gan, L., Jiao, B., Pan, H., & Yuan, H. (2025) · Sensors · doi:10.3390/s25185867

Estimation of Missing Stress Data for Structural Health Monitoring Based on Multitask Learning and Multisource Information

Huang, J.-X., Li, Q. S., & Han, X. (2026) · Journal of Structural Engineering · doi:10.1061/jsendh.steng-14959

Maintenance Modeling for a Multi-State System Under Competing Failures and Imperfect Repairs

Zhang, Y., & Meng, X. (2026) · Mathematics · doi:10.3390/math14020248

Deep Learning‐Based Missing Data Reconstruction in SHM Using Gated Dilated Convolution and GRU

The, H. T., Tien, T. B., & Ngoc, H. T. (2026) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.70257

Online Temperature Monitoring System for Semiconductor Automated Test Equipment Using Local Gaussian Process

Kim, J., Baik, S. M., Yu, D., Kim, J., & Ko, Y. M. (2026) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2026.3665465

Detection and Identification of Structural Damage Based on Bayesian Updating

Qian, Y. (2026) · Lecture notes in civil engineering · doi:10.1007/978-3-032-10649-0_20

Design of Unattended Rain and Snow Protection Device for Total Station Based on D-S Evidence Fusion

Jia, L., Liu, Y., Du, G., Li, X., Wang, Z., Lu, Y., & Zhang, Z. (2026) · Sensors · doi:10.3390/s26082327

Development of a systematic instrumentation methodology for continuous torque measurement in high-capacity shafts

Bižić, M., & Petrović, D. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6092

Operations-aware structural monitoring of a 46-year-old bridge using traffic-induced vibrations and frequency-domain analysis

Shehadeh, A., Alshboul, O., Hussein, M. O., Mahmoud, A. S., Dwairy, M., & Tatari, O. (2026) · Structural Health Monitoring · doi:10.1177/14759217261441771

Q-SOCO-Field: A Quantum-Enhanced Observation Capability Representation Framework for Earth Observation Sensor Planning

Li, J., Ding, X., Wang, K., Li, J., Jia, B., Wang, M., & Hu, C. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3689689

Experimental Study on Wind-Induced Vibration Control of Bridge Cables Based on Tuned Mass Dampers and Passive Suction and Jet Flow

He, Q., Li, X., Huang, Y., Min, X., Jin, Y., & Chen, W. (2026) · Applied Sciences · doi:10.3390/app16104893

A novel machine learning assisted weight measurement system based on bending loss of optical fiber by using image processing

Sarkar, A., Hazarika, N., Roy, R. K., & Bezboruah, T. (2026) · Measurement · doi:10.1016/j.measurement.2026.121969

Damage Identification of Truss Bridge Under Temperature Variations Based on Stiffness Separation Method

Xiao, F., Gong, Y., & Xiang, Y. (2026) · Sensors · doi:10.3390/s26123791

Multi-Level Spatial Design Decision-Making Model for Block Caving Systems in Super-Large Open-Pit Mines

Wang, Q., Cui, G.-Y., Sun, G.-Q., Ding, B., Ma, Z.-G., Yang, J.-M., Gong, P., Liu, J., & Zhu, H. (2026) · Applied Sciences · doi:10.3390/app16136753

Bayesian multi-model time series approach for structural health monitoring with sequential updates

Drangsfeldt, C. A., Avendaño-Valencia, L. D., & Lützen, M. (2026) · Data-Centric Engineering · doi:10.1017/dce.2026.10062

Bayesian structural model updating under prior misspecification using conditional normalizing flows

Itoi, T., Takeda, T., Lee, S., & Yaoyama, T. (2026) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2026.2706262

Torque-Aware Path Planning for Robot Manipulators Using Artificial Potential Fields

Miandoab, E. M., Mirshabani, S. M., Mozaffari, S., & Alirezaee, S. (2026) · IEEE Access · doi:10.1109/access.2026.3719210

Lightweight Convolutional Neural Networks for Wheat Disease Detection and Classification: A Review and Comparative Study From Field to Storage

Djakhdjakha, L., Amrouch, S., Abdelmoumène, H., Bouacida, I., & Farou, B. (2026) · IEEE Access · doi:10.1109/access.2026.3721950

Robotics integration in structural health monitoring in the Saudi construction projects

Rabi, M., Al‐Kheetan, M. J., Alazmi, Y. H., & Al-Zu’bi, M. (2026) · Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction · doi:10.1680/jsmic.26.00011

Mechanics-heteroscedastic probabilistic model for shear strength of prestressed concrete beams with shear reinforcements

Zhou, Y., Li, B., & Yu, Z. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.123852

Optimization-Driven Cluster-Weighted Clusterwise Linear Regression

Taheri, S., Bagirov, A., Perera, N. A., Sultanova, N., & Abdollahian, M. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3850158

Acceleration response reconstruction of a television tower via deep learning with multi-scale feature fusion

Bao, C., Yu, R., 尚绪强, Xiaotong, M., & Lv, J. (2026) · Structures · doi:10.1016/j.istruc.2026.113194

From track to impact: a review of multi-hazard typhoon forecasting and resilience

Hong, Y.-Y., & Medina, D. J. (2026) · Climate Services · doi:10.1016/j.cliser.2026.100741

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

Wang, Q., Wang, C.-B., Ma, Z., Chen, W., Ni, Y., Wang, C., Yan, B.-G., & Guan, P.-X. (2022) · Structural Health Monitoring · doi:10.1177/14759217221079529 · Cited by 52 · see citing works

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

Wang, Q., Dai, Y., Ma, Z., Wang, J., Lin, J., Ni, Y., Ren, W., Jiang, J. D., Yang, X., & Yan, J.-R. (2023) · Structural Health Monitoring · doi:10.1177/14759217231170316 · Cited by 54 · see citing works

Modelling and forecasting of SHM strain measurement for a large-scale suspension bridge during typhoon events using variational heteroscedastic Gaussian process

Wang, Q.-A., Zhang, C., Ma, Z.-G., & Ni, Y.-Q. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2021.113554 · Cited by 95 · see citing works

Uncertainty-aware, high-precision multi-step prediction of structural health monitoring sensor streams under extreme typhoon events: an enhanced Bayesian dynamic linear model leveraging the kernel regression basis function for severe environmental adaptation

Wang, Q., Huang, X., Ni, Y., Feng, W.-S., Wang, J., Qiu, S., Zhou, P., Li, J., & Luo, Y. (2025) · Measurement · doi:10.1016/j.measurement.2025.120050 · Cited by 32 · see citing works

Empirical Validation of Bayesian Dynamic Linear Models in the Context of Structural Health Monitoring

Goulet, J.-A., & Koo, K. Y. (2017) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001190 · Cited by 39 · see citing works