Establishing Efficacy of Machine Learning Techniques for Vulnerability Information of Tubular Buildings

Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis

Baker, J. W. (2014) · Earthquake Spectra · doi:10.1193/021113eqs025m · Cited by 1,593 · see citing works

Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques

Mangalathu, S., & Jeon, J. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.01.008 · Cited by 366 · see citing works

On the application of machine learning techniques to derive seismic fragility curves

Kiani, J., Camp, C. V., & Pezeshk, S. (2019) · Computers & Structures · doi:10.1016/j.compstruc.2019.03.004 · Cited by 315 · see citing works

Quantifying and communicating uncertainty in seismic risk assessment

Ellingwood, B. R., & Kinali, K. (2008) · Structural Safety · doi:10.1016/j.strusafe.2008.06.001 · Cited by 256 · see citing works

Seismic fragility analysis of low-rise unreinforced masonry structures

Park, J., Towashiraporn, P., Craig, J. I., & Goodno, B. J. (2008) · Engineering Structures · doi:10.1016/j.engstruct.2008.07.021 · Cited by 186 · see citing works

Empirical fragility curves for Italian residential RC buildings

Rosti, A., Gaudio, C. D., Rota, M., Ricci, P., Ludovico, M. D., Penna, A., & Verderame, G. M. (2020) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-020-00971-4 · Cited by 168 · 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

Fragility-based assessment of typical mid-rise and low-rise RC buildings in Turkey

Erberik, M. A. (2007) · Engineering Structures · doi:10.1016/j.engstruct.2007.07.016 · Cited by 150 · see citing works

Topology optimization via machine learning and deep learning: a review

Shin, S., Shin, D., & Kang, N. (2023) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwad072 · Cited by 146 · see citing works

Design for Manufacture and Assembly of Digital Fabrication and Additive Manufacturing in Construction: A Review

Prasittisopin, L., & Tuvayanond, W. (2023) · Buildings · doi:10.3390/buildings13020429 · Cited by 95 · see citing works

Development of fragility functions for geotechnical constructions: Application to cantilever retaining walls

Argyroudis, S. A., Kaynia, A. M., & Pitilakis, K. D. (2013) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2013.02.014 · Cited by 93 · see citing works

Bayesian‐optimized unsupervised learning approach for structural damage detection

Eltouny, K., & Liang, X. P. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12680 · Cited by 86 · see citing works

Unsupervised machine and deep learning methods for structural damage detection: A comparative study

Wang, Z., & Cha, Y. (2022) · Engineering Reports · doi:10.1002/eng2.12551 · Cited by 71 · see citing works

An analytical framework for seismic fragility analysis of RC high-rise buildings

Ji, J., Elnashai, A. S., & Kuchma, D. A. (2007) · Engineering Structures · doi:10.1016/j.engstruct.2007.08.026 · Cited by 70 · see citing works

Finite-Element Limit Analysis of the Tucker High School Gymnasium Roof Failure

Laursen, P. T., & Saliklis, E. P. (2012) · doi:10.1061/(asce)ae.1943- · Cited by 64 · see citing works

A novel deep unsupervised learning-based framework for optimization of truss structures

Mai, H. T., Lieu, Q. X., Kang, J., & Lee, J. (2022) · Engineering With Computers · doi:10.1007/s00366-022-01636-3 · Cited by 61 · see citing works

Machine learning approach for investigating compressive strength of self-compacting concrete containing supplementary cementitious materials and recycled aggregate

Huang, P., Dai, K., & Yu, X. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107904 · Cited by 56 · see citing works

Damage Probability Matrices and Empirical Fragility Curves From Damage Data on Masonry Buildings After Sarpol-e-zahab and Bam Earthquakes of Iran

Biglari, M., & Formisano, A. (2020) · Frontiers in Built Environment · doi:10.3389/fbuil.2020.00002 · Cited by 56 · see citing works

Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength

Hameed, M. M., AlOmar, M. K., Baniya, W. J., & Alsaadi, M. A. (2021) · Asian Journal of Civil Engineering · doi:10.1007/s42107-021-00362-3 · Cited by 52 · see citing works

Emerging trends in the growth of structural systems for tall buildings

Memon, S. A., Zain, M., Zhang, D., Rehman, S. K. U., Usman, M., & Lee, D. H. (2020) · Journal of Structural Integrity and Maintenance · doi:10.1080/24705314.2020.1765270 · Cited by 46 · see citing works

A novel unsupervised real‐time damage detection method for structural health monitoring using machine learning

Shi, S., Du, D., Mercan, O., Kalkan, E., & Wang, S. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3042 · Cited by 43 · see citing works

Do seismic intensity measures (IMs) measure up?

Grigoriu, M. D. (2016) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2016.09.002 · Cited by 41 · see citing works

Unsupervised fuzzy neural networks for damage detection of structures

Wen, C. M., Hung, S., Huang, C., & Jan, J. C. (2005) · Structural Control and Health Monitoring · doi:10.1002/stc.116 · Cited by 40 · see citing works

Post-earthquake seismic capacity estimation of reinforced concrete bridge piers using Machine learning techniques

Todorov, B., & Billah, A. H. M. M. (2022) · Structures · doi:10.1016/j.istruc.2022.05.067 · Cited by 37 · see citing works

Effective Modeling for Construction Activities of Recycled Aggregate Concrete Using Artificial Neural Network

Kittinaraporn, W., Tuprakay, S., & Prasittisopin, L. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002246 · Cited by 35 · see citing works

Design and Construction Method of a 3D Concrete Printing Self-Supporting Curvilinear Pavilion

Prasittisopin, L., Sakdanaraseth, T., & Horayangkura, V. (2021) · Journal of Architectural Engineering · doi:10.1061/(asce)ae.1943-5568.0000485 · Cited by 35 · see citing works

Seismic Vulnerability Assessment of School Buildings in Seismic Zone 4 of Pakistan

Zain, M., Usman, M., Farooq, S. H., & Mehmood, T. (2019) · Advances in Civil Engineering · doi:10.1155/2019/5808256 · Cited by 30 · see citing works

Performance Characteristics of Blended Cementitious Systems Incorporating Chemically Transformed Rice Husk Ash

Prasittisopin, L., & Trejo, D. (2017) · Advances in Civil Engineering Materials · doi:10.1520/acem20160001 · Cited by 29 · see citing works

Empirical fragility curves: The effect of uncertainty in ground motion intensity

Ioannou, I., Chandler, R. E., & Rossetto, T. (2019) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2019.105908 · Cited by 28 · see citing works

A simplified fragility analysis methodology for containment structure subjected to overpressure condition

Jin, S., Li, Z., Dong, Z., Lan, T., & Gong, J. (2020) · International Journal of Pressure Vessels and Piping · doi:10.1016/j.ijpvp.2020.104104 · Cited by 27 · see citing works

Optimal earthquake intensity measures for probabilistic seismic demand models of ARP1400 reactor containment building

Nguyen, D., Thusa, B., Azad, M. S., Tran, V., & Lee, T. (2021) · Nuclear Engineering and Technology · doi:10.1016/j.net.2021.06.034 · Cited by 27 · see citing works

Machine Learning Approach for Sequence Clustering with Applications to Ground-Motion Selection

Zhang, R., Hajjar, J. F., & Sun, H. (2020) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0001766 · Cited by 26 · see citing works

Structural probabilistic seismic risk analysis and damage prediction based on artificial neural network

Jia, D.-W., & Wu, Z. (2022) · Structures · doi:10.1016/j.istruc.2022.05.056 · Cited by 23 · see citing works

Seismic fragility analysis of a large-scale frame structure with local nonlinearities using an efficient reduced-order Newton-Raphson method

Sun, B., Zhang, Y., Dai, D., Wang, L., & Ou, J. (2022) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2022.107559 · Cited by 23 · see citing works

Nonlinear dynamic responses of ballasted railway tracks using concrete sleepers incorporated with reinforced fibres and pre-treated crumb rubber

Raj, A., Ngamkhanong, C., Prasittisopin, L., & Kaewunruen, S. (2023) · Nonlinear Engineering · doi:10.1515/nleng-2022-0320 · Cited by 22 · see citing works

Load Carrying Capacity and Failure Mode of 3D Printing Mortar Wall Panel Under Axial Compression Loading

Jiramarootapong, P., Prasittisopin, L., Snguanyat, C., Tanapornraweekit, G., & Tangtermsirikul, S. (2020) · Rilem bookseries · doi:10.1007/978-3-030-49916-7_65 · Cited by 21 · see citing works

Mainshock-aftershock fragility surfaces analysis of reinforced concrete frame structures using a double incremental dynamic analysis approach

Song, P., Wang, C., & Sun, Q. (2023) · Structures · doi:10.1016/j.istruc.2023.07.058 · Cited by 19 · see citing works

Story-wise assessment of seismic behavior and fragility analysis of R/C frames considering the effect of masonry infills

Vasileiadis, V., Kostinakis, K., & Athanatopoulou, A. (2022) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2022.107714 · Cited by 18 · see citing works

A Framework with reduced computational burden for Seismic Fragility Assessment of Reinforced Concrete Buildings in High-Intensity Seismic Zones

Zain, M., Usman, M., & Farooq, S. H. (2021) · Structures · doi:10.1016/j.istruc.2021.06.050 · Cited by 15 · see citing works

Machine Learning-Based Fragility Assessment of Reinforced Concrete Buildings

Rasheed, A., Usman, M., Zain, M., & Iqbal, N. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5504283 · Cited by 15 · see citing works

Effect of masonry infill on analytical fragility response of RC frame school buildings in high seismic zone

Khan, M. W., Usman, M., Farooq, S. H., Zain, M., & Saleem, S. (2021) · Journal of Structural Integrity and Maintenance · doi:10.1080/24705314.2020.1865624 · Cited by 15 · see citing works

Seismic Fragility Analysis

Erberik, M. A. (2015) · Springer eBooks · doi:10.1007/978-3-642-36197-5_387-1 · Cited by 15 · see citing works

Seismic Fragility Assessment of Reinforced Concrete High-Rise Buildings Using the Uncoupled Modal Response History Analysis (UMRHA)

Zain, M., Anwar, N., Najam, F. A., & Mehmood, T. (2018) · Geotechnical, geological and earthquake engineering · doi:10.1007/978-3-319-78187-7_16 · Cited by 13 · see citing works

A simplified non-linear modelling strategy to generate fragility curves for old masonry buildings

Stocchi, A., Giry, C., Capdevielle, S., Zentner, I., Nayman, E., & Ragueneau, F. (2021) · Computers & Structures · doi:10.1016/j.compstruc.2021.106579 · Cited by 12 · see citing works

Seismic Fragility Analysis of Buildings Based on Double-Parameter Damage Models considering Soil-Structure Interaction

Zhai, P., Zhao, P., Lu, Y., Ye, C., & Xiong, F. (2019) · Advances in Materials Science and Engineering · doi:10.1155/2019/4592847 · Cited by 12 · see citing works

Progressive structural capacity loss assessment—A framework for modern reinforced concrete buildings

Zain, M., Usman, M., Farooq, S. H., & Hanif, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208149 · Cited by 10 · see citing works

Seismic Vulnerability Assessment: Lifelines

Pitilakis, K. D., & Argyroudis, S. A. (2014) · Springer eBooks · doi:10.1007/978-3-642-36197-5_255-1 · Cited by 9 · see citing works

Lightweight Concrete for Modular Floor Structure: Survey, Experiment, In-Field Study

Prasittisopin, L., Khosakitchalert, C., Vas‐Umnuay, P., & Pansuk, W. (2022) · Advances in Civil Engineering Materials · doi:10.1520/acem20220020 · Cited by 6 · see citing works

How 3D Printing Technology Makes Cities Smarter: A Review, Thematic Analysis, and Perspectives

Prasittisopin, L. (2024) · Smart Cities · doi:10.3390/smartcities7060135 · Cited by 73 · see citing works

Concrete Mix Design of Recycled Concrete Aggregate (RCA): Analysis of Review Papers, Characteristics, Research Trends, and Underexplored Topics

Prasittisopin, L., Tuvayanond, W., Kang, T. H. ‐., & Kaewunruen, S. (2025) · Resources · doi:10.3390/resources14020021 · Cited by 73 · see citing works

Enhancing Sustainable Railway Station Design in Tropical Climates: Insights from Thailand’s Architectural Theses and Case Studies

Tetiranont, S., Sadakorn, W., Rugkhapan, N. T., & Prasittisopin, L. (2024) · Buildings · doi:10.3390/buildings14030829 · Cited by 47 · see citing works

Active learning-based regional seismic risk assessment of high-speed railway bridges

Zheng, X., Wei, B., Jiang, L., Lai, Z., Chen, J., Chen, M., Xiao, B., Zhang, R., & Yang, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103470 · Cited by 47 · see citing works

Machine learning (ML) algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones

Zain, M., Dackermann, U., & Prasittisopin, L. (2024) · Structures · doi:10.1016/j.istruc.2024.107639 · Cited by 45 · see citing works

Machine learning (ML) and deep learning (DL) in sustainable concrete construction: review, trend and gap analyses

Prasittisopin, L. (2025) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2025.2574571 · Cited by 45 · see citing works

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Zain, M., Prasittisopin, L., Mehmood, T., Ngamkhanong, C., Keawsawasvong, S., & Thongchom, C. (2024) · Nonlinear Engineering · doi:10.1515/nleng-2022-0365 · Cited by 38 · see citing works

Efficient machine learning for strength prediction of ready-mix concrete production (prolonged mixing)

Tuvayanond, W., Kamchoom‬, V., & Prasittisopin, L. (2024) · Construction Innovation · doi:10.1108/ci-09-2023-0240 · Cited by 27 · see citing works

Structural Optimization of Trusses in Building Information Modeling (BIM) Projects Using Visual Programming, Evolutionary Algorithms, and Life Cycle Assessment (LCA) Tools

Yavan, F., Maalek, R., & Toğan, V. (2024) · Buildings · doi:10.3390/buildings14061532 · Cited by 26 · see citing works

Advancing urban thermal comfort: adaptive ensemble machine learning models for tropical climates

Kupwiwat, C.-T., Kaewunruen, S., & Prasittisopin, L. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115762 · Cited by 25 · see citing works

Optimizing Spectral Properties of Cesium Tungsten Bronze Films Doped with Silver Nanowires Based on the Machine Learning Method

He, Z., Xie, W., Zhang, S., Gao, Y., Elnaggar, A. Y., Ren, J., Azab, I. H. E., El-Bahy, Z. M., Yang, M., Zhang, H., et al. (2024) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.4c03634 · Cited by 20 · see citing works

Influence of trace Mn doping on the high-speed shear performance of lead-free alloy/copper solder joints: Experimental and first principles investigation

Yuan, M. X., Cai, S., Li, C., Wang, X., Liu, C., Qiao, Y., Pang, X., Elsharkawy, E. R., Liu, B., Zhang, J., et al. (2024) · Surfaces and Interfaces · doi:10.1016/j.surfin.2024.104477 · Cited by 19 · see citing works

Implementing the Sister City Policy: Perspectives from Thailand

Prasittisopin, L., Kitkuakul, P., Chotchakornpant, K., & Rugkhapan, N. T. (2024) · Nakhara Journal of Environmental Design and Planning · doi:10.54028/nj202423413 · Cited by 16 · see citing works

Modal-based fragility analysis of high-rise tubular structures: A methodology for vulnerability assessment

Zain, M., Ngamkhanong, C., Kang, T. H. ‐., Usman, M., & Prasittisopin, L. (2024) · Structures · doi:10.1016/j.istruc.2024.106289 · Cited by 14 · see citing works

Analysis of Space Efficiency in High-Rise Timber Residential Towers

Ilgın, H. E., & Aslantamer, Ö. N. (2024) · Applied Sciences · doi:10.3390/app14114337 · Cited by 13 · see citing works

Advancing civil infrastructure with digital twins: a review of applications and challenges

Kaveh, H., & Alhajj, R. (2025) · Journal of Civil Engineering and Management · doi:10.3846/jcem.2025.24921 · Cited by 12 · see citing works

A Machine Learning Model to Predict the Seismic Lifecycle Behavior of a Cross-Sea Cable-Stayed Bridge

Lu, P., Liu, Z., & Zhang, T. (2024) · Buildings · doi:10.3390/buildings14051190 · Cited by 12 · see citing works

Fire performance evaluation of 3D-Printed concrete walls: A combined full-scale and numerical modeling approach

Suphunsaeng, K., Prasittisopin, L., Pethrung, S., & Pansuk, W. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112296 · Cited by 10 · see citing works

Sustainable AI: Emerging Trends, Impacts, and Future Challenges

Philipo, A. G., Ning, H., Sarwatt, D. S., Mohamed, J. A., Yusufu, A. S., Shi, F., Urenje, S., & Ding, J. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3611272 · Cited by 9 · see citing works

Modal Decomposition and Genetic Algorithm–Based Vulnerability Evaluation of High‐Rise Structures—An Assessment of Structural Optimization Methodologies for Fragility Curves Development

Prasittisopin, L., Zain, M., Keawsawasvong, S., & Iqbal, M. Z. (2025) · The Structural Design of Tall and Special Buildings · doi:10.1002/tal.70032 · Cited by 8 · see citing works

AI-Driven Digital Twin Framework for Adaptive and Real-Time Structural Health Monitoring of Offshore Marine Structures

Riffat, J., Nazari, K., & Samaei, S. R. (2026) · Artificial Intelligence for Sustainable Cities · doi:10.65582/aifsc.2026.006 · Cited by 8 · see citing works

Study on the damage characteristics of high-temperature superconducting cable insulation under air gap discharge

Pan, X., Li, Z., Wang, G., Fallatah, A. M., Yuan, M., Zhang, X., Tan, D., Ren, J., Almalki, A. S. A., Ibrahim, M. M., et al. (2024) · Journal of Materials Science Materials in Electronics · doi:10.1007/s10854-024-13642-w · Cited by 4 · see citing works

Resilience-based seismic assessment of reinforced concrete building systems through representative structural modeling

Xu, W., Yan, G., Mohammed, K. J., You, D., Ghoniem, R. M., & Ali, H. E. (2026) · Structures · doi:10.1016/j.istruc.2026.111124 · Cited by 4 · see citing works

Beyond the hype: quantifying the impact of VR visualization on client preferences and price perception in AEC client pitching

Tetiranont, S., Sadakorn, W., Sreshthaputra, A., Aniwattanapong, D., & Prasittisopin, L. (2024) · Architectural Science Review · doi:10.1080/00038628.2024.2410734 · Cited by 4 · see citing works

A Systematic Review on Intelligent Prediction of Inorganic Building Materials Performance

Li, M., Zhang, Z., Zeng, X., & He, Z. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00843-2 · Cited by 3 · see citing works

Machine Learning Models for Predicting Seismic Response of a Novel Two-Stage Friction Pendulum Isolated Bridge Structure

Akhlaq, H., Peng, T., Jitteh, K., & Khan, M. S. (2025) · IEEE Access · doi:10.1109/access.2025.3580065 · Cited by 3 · see citing works

Fragility curve modifiers for dual-frame systems in various site classes incorporating nonlinear SSI effects

Mathews, M., Jayalekshmi, B. R., & Venkataramana, K. (2025) · Journal of Structural Integrity and Maintenance · doi:10.1080/24705314.2025.2503118 · Cited by 2 · see citing works

Nanotechnology for calcium aluminate cement: thematic analysis

Prasittisopin, L. (2025) · REVIEWS ON ADVANCED MATERIALS SCIENCE · doi:10.1515/rams-2025-0180 · Cited by 2 · see citing works

Machine Learning-Enabled Real-Time Prediction of Drying Shrinkage in Fly Ash-Modified Cementitious Materials

Kupwiwat, C.-T., & Prasittisopin, L. (2026) · ACS Omega · doi:10.1021/acsomega.6c04596 · Cited by 2 · see citing works

A novel machine learning technique for peak load prediction in concrete-filled steel column based on Kolmogorov–Arnold model

Nguyen, X. T., Brahim, A. O., Khatir, S., Luan, T. M., & Cuong‐Le, T. (2025) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2025.2518470 · Cited by 1 · see citing works

Optimization method for epoxy concrete formula based on GA-BP neural network

Yang, S., & Zhang, G. (2025) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2025.2526078

Industry 4.0–5.0 research in construction materials: a bibliometric analysis toward circular and beyond-net-zero built environments

Prasittisopin, L. (2026) · Built Environment Project and Asset Management · doi:10.1108/bepam-08-2025-0238

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Zain, M., Prasittisopin, L., Mehmood, T., Ngamkhanong, C., Keawsawasvong, S., & Thongchom, C. (2024) · Nonlinear Engineering · doi:10.1515/nleng-2022-0365 · Cited by 38 · see citing works

Modal Decomposition and Genetic Algorithm–Based Vulnerability Evaluation of High‐Rise Structures—An Assessment of Structural Optimization Methodologies for Fragility Curves Development

Prasittisopin, L., Zain, M., Keawsawasvong, S., & Iqbal, M. Z. (2025) · The Structural Design of Tall and Special Buildings · doi:10.1002/tal.70032 · Cited by 8 · see citing works

Modal-based fragility analysis of high-rise tubular structures: A methodology for vulnerability assessment

Zain, M., Ngamkhanong, C., Kang, T. H. ‐., Usman, M., & Prasittisopin, L. (2024) · Structures · doi:10.1016/j.istruc.2024.106289 · Cited by 14 · see citing works

Machine learning-based seismic fragility curves of regular infilled RC frames

He, D., Cheng, X., Liu, H., Li, Y., Zhang, H., & Ding, Z. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111545 · Cited by 7 · see citing works

MACHINE LEARNING-BASED IDENTIFICATION OF VULNERABILITY FACTORS FOR MASONRY BUILDINGS IN AGGREGATE: THE HISTORICAL CENTRE OF CASENTINO HIT BY THE 2009 L’AQUILA EARTHQUAKE

Pinasco, S., Lagomarsino, S., Carocci, C. F., Coraddu, A., Oneto, L., & Cattari, S. (2023) · COMPDYN Proceedings · doi:10.7712/120123.10472.21116 · Cited by 3 · see citing works