- Samuel, A. L., 1959, Some studies in machine learning using the game of checkers, IBM Journal of Research and Development, 3(3), 535–554.
- Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J. and Gerstoft, P., 2018, Machine learning in seismology: Turning data into insights, Seismological Research Letters, 90(1), 3–14.
- Adeli, H., 2001, Neural networks in civil engineering: 1989-2000, Computer-Aided Civil and Infrastructure Engineering, 16(2), 126–142.
- Kicinger, R., Arciszewski, T. and De Jong, K., 2005, Evolutionary computation and structural design: A survey of the state-of-the-art, Computers and Structures 83(23–24), 1943–1978.
- Shahin, M. A., 2014, A review of artificial intelligence applications in shallow foundations, International Journal of Geotechnical Engineering, 9(1), 49–60.
- Shahin, M. A., 2016, State-of-the-art review of some artificial intelligence applications in pile foundations, Geoscience Frontiers, 7(1), 33–44.
- Kusiak, A., and Lee, H., 1996, Neural computing based design of components for cellular manufacturing, International Journal of Production Research, 34(7), 1777-1790.
- LeCun, Y, Bengio, Y. and Hinton, G., 2015, Deep learning, Nature, 521, 436–444.
- Hosmer, D. W., and Lemeshow, S., 2000, Applied Logistic Regression, 2nd Ed. New York: John Wiley. Housner GW, Bergman LA, Caughey TK, Chassiakos AG, Claus RO, Masri SF, Skelton RE, Soong TT, Spencer BF and Yao JTP (1997) Structural control: Past, present, and future. Journal of Engineering Mechanics 123(9): 897–971.
- Douglas, J., 2003, Earthquake ground motion estimation using strong-motion records: A review of equations for the estimation of peak ground acceleration and response spectral ordinates, Earth Science Reviews, 61(1–2), 43–104.
-Gulkan, P., and Kalkan, E., 2002, Attenuation modeling of recent earthquakes in Turkey, Journal of Seismology, 6(3), 397–409.
- Amiri, G. G., Khorasani, M., Hessabi, R. M. and Amrei, S. A. R., 2010, Ground-motion prediction equations of spectral ordinates and arias intensity for Iran, Journal of Earthquake Engineering, 14(1), 1–29.
- Akhani, M., Kashani, A. R., Mousavi, M. and Gandomi, A. H., 2019, A hybrid computational intelligence approach to predict spectral acceleration, Measurement, 138, 578–589.
-Ancheta, T. D., Darragh, R. B., Stewart, J. P., Seyhan, E., Silva, W. J., Chiou, B. S. J., Wooddell, K. E., Graves, R. W., Kottke, A., Boore, D. M., Kishida, T. and Donahue, J., 2014, NGA-West2 database, Earthquake Spectra, 30(3), 989–1005.
- Chiou, B., Darragh, R., Gregor, N., and Silva, W., 2008, NGA project strong-motion database, Earthquake Spectra, 24(1), 23–44.
- Khosravikia, F., Potter, A., Prakhov, V., Zalachoris, G., Cheng, T., Tiwari, A., Clayton, P., Cox, B., Rathje, E., Williamson, E., Paine, J., and Frohlich, C., 2018, Seismic Vulnerability and Post-event Actions for Texas Bridge Infrastructure, Austin, TX: The University of Texas at Austin.
- Cabalar, A. F., and Cevik, A., 2009, Genetic programming-based attenuation relationship: An application of recent earthquakes in turkey. Computers and Geosciences, 35(9), 1884–1896.
- Javan-Emrooz, H., Eskandari-Ghadi, M., and Mirzaei, N., 2018, Prediction equations for horizontal and vertical PGA, PGV, and PGD in northern Iran using prefix gene expression programming, Bulletin of the Seismological Society of America, 108(4), 2305–2332.
- Markicˇ, S.ˇ and Stankovski, V., 2013, An equation-discovery approach to earthquake-ground-motion prediction, Engineering Applications of Artificial Intelligence, 26(4), 1339–1347.
- Derakhshani, A., and Foruzan, A. H., 2019, Predicting the principal strong ground motion parameters: A deep learning approach, Applied Soft Computing, 80, 192–201.
-Alavi, A. H., and Gandomi, A. H., 2011, Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing, Computers and Structures, 89, 2176–2194.
-Thomas, S., Pillai, G. N., and Pal, K., 2017, Prediction of peak ground acceleration using e-SVR, n-SVR and Ls-SVR algorithm. Geomatics, Natural Hazards and Risk, 8(2), 177–193.
- Hamze-Ziabari, S. M., and Bakhshpoori, T., 2018, Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5# and CART algorithms, Applied Soft Computing Journal, 68, 147–161.
- Trugman, D. T. and Shearer, P. M., 2018, Strong correlation between stress drop and peak ground acceleration for recent M 1–4 earthquakes in the San Francisco bay area, Bulletin of the Seismological Society of America, 108(2), 929–945.
- Alimoradi, A., and Beck, J. L., 2015, Machine-learning methods for earthquake ground motion analysis and simulation, Journal of Engineering Mechanics, 141(4), 04014147.
- Luo, H., and Paal, S. G., 2019, A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments, Computer-Aided Civil and Infrastructure Engineering, 34, 935–950.
- Domingos, P., 2012, A few useful things to know about machine learning, Communications of the ACM, 55(10), 78.
- Garcı´a, S. R., Romo, M. P. and Botero, E., 2008, A neurofuzzy system to analyze liquefaction-induced lateral spread, Soil Dynamics and Earthquake Engineering, 28(3), 169–180.
- Salehi, H., and Burguen˜o, R., 2018, Emerging artificial intelligence methods in structural engineering, Engineering Structures, 171, 170–189.
- Memarzadeh, M., and Pozzi, M., 2019, Model-free reinforcement learning with model-based safe exploration: Optimizing adaptive recovery process of infrastructure systems, Structural Safety, 80, 46–55
- Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N. F., and Kumar, V., 2017, Theory-guided data science: A new paradigm for scientific discovery from data, IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318–2331.
- Wagner, N., and Rondinelli, J. M., 2016, Theory-guided machine learning in materials science, Frontiers in Materials, 3, 1–9
- Xie, Y., Zhang J., Des Roches, R. and Padgett, J. E., 2019a, Seismic fragilities of single-column highway bridges with rocking column-footing, Earthquake Engineering & Structural Dynamics, 48, 843–86.
- Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., and Walsh, A., 2018, Machine learning for molecular and materials science, Nature, 559(7715), 547–555.
- Mangalathu, S., and Jeon, J. S., 2018, Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques, Engineering Structures, 160, 85–94.
- Sichani, M. E., 2018, Seismic risk assessment of vertical concrete dry casks, PhD Dissertation, Rice University, Houston, TX.
- Raissi, M., and Karniadakis, G. E., 2018, Hidden physics models: Machine learning of nonlinear partial differential equations, Journal of Computational Physics, 357, 125–141.