Li, S., Tang, X., Guo, W., Li, Y., Chen, D., Zhang, J., Zhang, Q., Xu, H., Wan, Z., Premadasa, R., Lu, H., Zhang, Q., Salehi, H., & Jiao, P. (2024). “Numerical simulations of piezoelectricity and triboelectricity: From materials, structures to devices”, Applied Materials Today, 37, 102092.
https://doi.org/10.1016/j.apmt.2024.102092


Zhu, R, Egbe, K., Salehi, H., Shi, Z., & Jiao, P. (2024). “Eco-friendly 3D printed concrete with fine aggregate replacements: Fabrication, characterization and machine learning prediction”, Construction and Building Materials, 413, 134905.
https://doi.org/10.1016/j.conbuildmat.2024.134905 


Salehi, H., Gorodetsky, A., Solhmirzaei, R., & Jiao, P. (2023). “High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition”, Engineering Applications of Artificial Intelligence, 125, 106659.
https://doi.org/10.1016/j.engappai.2023.106659


Chen, Z., Lin, Y.T., Salehi, H., Che, Z., Zhu, Y., Ding, J., Sheng, B., Zhu, R. and Jiao, P. (2023). “Advanced Fabrication of Mechanical Metamaterials Based on Micro/Nanoscale Technology”, Advanced Engineering Materials.
https://doi.org/10.1002/adem.202300750


Solhmirzaei, R., Salehi, H., & Kodur, V. (2022). “Predicting flexural capacity of ultrahigh-performance concrete beams: machine learning–based approach”, Journal of Structural Engineering, 148(5), 04022031.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0003320


Santos, C. N., Cetin, K. S., & Salehi, H. (2022). “Energy-efficient technology retrofit investment behaviors of Midwest households in lower and higher income regions”, Sustainable Cities and Society, 86, 104141.
https://doi.org/10.1016/j.scs.2022.104141


He, Z., Li, W., Salehi, H., Zhang, H., Zhou, H., & Jiao, P. (2022). “Integrated structural health monitoring in bridge engineering”, Automation in Construction, 136, 104168.
https://doi.org/10.1016/j.autcon.2022.104168


Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N., & Alavi, A. H. (2021). “A comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research trends”, Engineering Structures, 234, 111963.
https://doi.org/10.1016/j.engstruct.2021.111963


Solhmirzaei, R., Salehi, H., & Kodur, V. (2021). “Response Prediction of Ultra-High-Performance Concrete Beams using Machine Learning”, ACI Special Publication, 350, 113-122.
https://doi.org/10.14359/51734317


Zhang, Q., Barri, K., Jiao, P., Salehi, H., & Alavi, A. H. (2021). “Genetic programming in civil engineering: Advent, applications and future trends”, Artificial Intelligence Review, 54, 1863-1885.
https://doi.org/10.1108/02644401111118132


De Moraes, E. A. B., Salehi, H., & Zayernouri, M. (2021). “Data-driven failure prediction in brittle materials: A phase field-based machine learning framework”, Journal of Machine Learning for Modeling and Computing, 2(1).
https://doi.org/10.1615/JMachLearnModelComput.2021034062


Solhmirzaei, R., Salehi, H., Kodur, V., & Naser, M. Z. (2020). “Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams”, Engineering structures, 224, 111221.
https://doi.org/10.1016/j.engstruct.2020.111221


Naser, M. Z., & Salehi, H. (2020). “Machine learning-driven assessment of fire-induced concrete spalling of columns”, ACI Materials Journal, 117(6), 7-16.
https://doi.org/10.14359/51728120


Salehi, H., Das, S., Biswas, S., & Burgueño, R. (2019). “Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals”, Expert Systems with Applications, 135, 259-272.
https://doi.org/10.1016/j.eswa.2019.05.051


Salehi, H., Biswas, S., & Burgueño, R. (2019). “Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations”, Engineering Applications of Artificial Intelligence, 86, 136-153.
https://doi.org/10.1016/j.engappai.2019.08.004


Salehi, H., & Burgueño, R. (2019). “Pattern recognition framework using asynchronous discrete binary data for condition and damage assessment in plate-like structures”, Journal of Intelligent Material Systems and Structures, 30(8), 1200-1215.
https://doi.org/10.1177/1045389X19835948


Salehi, H., Das, S., Chakrabartty, S., Biswas, S., & Burgueño, R. (2019). “An algorithmic framework for reconstruction of time-delayed and incomplete binary signals from an energy-lean structural health monitoring system”, Engineering Structures, 180, 603-620.
https://doi.org/10.1016/j.engstruct.2018.11.072


Salehi, H., Chakrabartty, S., Biswas, S., & Burgueno, R. (2019). “Localized damage identification in plate-like structures using self-powered sensor data: A pattern recognition strategy”, Measurement, 135, 23-38.
https://doi.org/10.1016/j.measurement.2018.11.023


Salehi, H., Das, S., Chakrabartty, S., Biswas, S., & Burgueño, R. (2018). “Damage identification in aircraft structures with self‐powered sensing technology: A machine learning approach”, Structural Control and Health Monitoring, 25(12), e2262.
https://doi.org/10.1002/stc.2262


Salehi, H., Das, S., Chakrabartty, S., Biswas, S., & Burgueño, R. (2018). “Structural damage identification using image‐based pattern recognition on event‐based binary data generated from self‐powered sensor networks”, Structural Control and Health Monitoring, 25(4), e2135.
https://doi.org/10.1002/stc.2135


Salehi, H., & Burgueño, R. (2018). “Emerging artificial intelligence methods in structural engineering”, Engineering structures, 171, 170-189.
https://doi.org/10.1016/j.engstruct.2018.05.084


Das, S., Salehi, H., Shi, Y., Chakrabartty, S., Burgueno, R., & Biswas, S. (2017). “Towards packet-less ultrasonic sensor networks for energy-harvesting structures”, Computer Communications, 101, 94-105.
https://doi.org/10.1016/j.comcom.2016.11.001


Salehi, H., Taghikhany, T., & Yeganeh Fallah, A. (2014). “Seismic protection of vulnerable equipment with semi-active control by employing robust and clipped-optimal algorithms”, International Journal of Civil Engineering, 12(4), 413-428.