Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.
AdeliH (2001) Neural networks in civil engineering: 1989-2000. Computer-Aided Civil and Infrastructure Engineering16(2): 126–142.
2.
AdeliHJiangX (2006) Dynamic fuzzy wavelet neural network model for structural system identification. Journal of Structural Engineering132(1): 102–111.
3.
AkbasBShenJSabolTA (2011) Estimation of seismic-induced demands on column splices with a neural network model. Applied Soft Computing11(8): 4820–4829.
4.
AkhaniMKashaniARMousaviMGandomiAH (2019) A hybrid computational intelligence approach to predict spectral acceleration. Measurement138: 578–589.
5.
AkkarSSandıkkayaMAŞenyurtMSisiAAAyBTraversaPDouglasJCottonFLuziLHernandezBGodeyS (2014) Reference database for seismic ground-motion in Europe (RESORCE). Bulletin of Earthquake Engineering12(1): 311–339.
6.
AlaviAHGandomiAH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Computers and Structures89: 2176–2194.
7.
AlaviAHGandomiAHModaresnezhadMMousaviM (2011) New ground-motion prediction equations using multi expression programing. Journal of Earthquake Engineering15(4): 511–536.
8.
AlimoradiABeckJL (2015) Machine-learning methods for earthquake ground motion analysis and simulation. Journal of Engineering Mechanics141(4): 04014147.
9.
AllaliSAAbedMMebarkiA (2018) Post-earthquake assessment of buildings damage using fuzzy logic. Engineering Structures166: 117–127.
10.
AlvanitopoulosPFAndreadisIElenasA (2010) Neuro-fuzzy techniques for the classification of earthquake damages in buildings. Measurement43(6): 797–809.
11.
Amezquita-SanchezJPValtierra-RodriguezMAldwaikMAdeliH (2016) Neurocomputing in civil infrastructure. Scientia Iranica23(6): 2417–2428.
12.
AmiriGGKhorasaniMHessabiRMAmreiSAR (2010) Ground-motion prediction equations of spectral ordinates and arias intensity for Iran. Journal of Earthquake Engineering14(1): 1–29.
AndriotisCPPapakonstantinouKG (2019) Managing engineering systems with large state and action spaces through deep reinforcement learning. Reliability Engineering and System Safety191: 106483.
15.
AokiTCeravoloRDe StefanoAGenoveseCSabiaD (2002) Seismic vulnerability assessment of chemical plants through probabilistic neural networks. Reliability Engineering and System Safety77(3): 263–268.
16.
BakhshiHBagheriAGhodratiAmiri GBarkhordariMA (2014) Estimation of spectral acceleration based on neural networks. Proceedings of the Institution of Civil Engineers—Structures and Buildings167(8): 457–468.
17.
Bani-HaniKGhaboussiJ (1998) Neural networks for structural control of a benchmark problem, active tendon system. Earthquake Engineering and Structural Dynamics27(11): 1225–1245.
18.
Bani-HaniKGhaboussiJSchneiderSP (1999a) Experimental study of identification and control of structures using neural network. Part 1: Identification. Earthquake Engineering and Structural Dynamics28(9): 995–1018.
19.
Bani-HaniKGhaboussiJSchneiderSP (1999b) Experimental study of identification and control of structures using neural network. Part 2: Control. Earthquake Engineering and Structural Dynamics28(9): 1019–1039.
20.
Bani-HaniKAShebanMA (2006) Semi-active neuro-control for base-isolation system using magnetorheological (MR) dampers. Earthquake Engineering and Structural Dynamics35(9): 1119–1144.
21.
BartlettSFYoudTL (1995) Empirical prediction of liquefaction-induced lateral spread. Journal of Geotechnical Engineering121(4): 316–329.
22.
BaziarMHGhorbaniA (2005) Evaluation of lateral spreading using artificial neural networks. Soil Dynamics and Earthquake Engineering25(1): 1–9.
23.
BirdJFBommerJJCrowleyHPinhoR (2006) Modelling liquefaction-induced building damage in earthquake loss estimation. Soil Dynamics and Earthquake Engineering26(1): 15–30.
24.
BishopCM (2006) Pattern Recognition and Machine Learning (ed JordanMKleinbergJScholkopfB). New York: Springer.
25.
BlatmanGSudretB (2010) An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probabilistic Engineering Mechanics25(2): 183–197.
26.
BooreDMAtkinsonGM (2008) Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01 s and 10.0 s. Earthquake Spectra24(1): 99–138.
27.
BooreDMStewartJPSeyhanEAtkinsonGM (2014) NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes. Earthquake Spectra30(3): 1057–1085.
28.
BoulangerRWIdrissIM (2014) CPT and SPT based liquefaction triggering procedures. Report No. UCD/CGM-14/01, April. Davis, CA: University of California at Davis.
29.
BoxGEPHunterJS (1957) Multi-factor experimental designs for exploring response surfaces. The Annals of Mathematical Statistics28: 195–241.
30.
BozorgvarMZahraiSM (2019) Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm. Journal of Vibration and Control25(2): 273–285.
31.
BrewickPYuTJohnsonESatoESasakiTChristensonR (2019) NEESR E-Defense Base Isolation 2013: System Identification. DesignSafe-CI. DOI: 10.17603/ds2-3d7a-x318.
32.
BrownASYangHTY (2001) Neural networks for multiobjective adaptive structural control. Journal of Structural Engineering127(2): 203–210.
33.
BurattiNFerracutiBSavoiaM (2010) Response Surface with random factors for seismic fragility of reinforced concrete frames. Structural Safety32(1): 42–51.
34.
BurtonHVSreekumarSSharmaMSunH (2017) Estimating aftershock collapse vulnerability using mainshock intensity, structural response and physical damage indicators. Structural Safety68: 85–96.
35.
ButlerKTDaviesDWCartwrightHIsayevOWalshA (2018) Machine learning for molecular and materials science. Nature559(7715): 547–555.
36.
CabalarAFCevikA (2009) Genetic programming-based attenuation relationship: An application of recent earthquakes in turkey. Computers and Geosciences35(9): 1884–1896.
37.
CadiniFSantosFZioE (2014) An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability. Reliability Engineering and System Safety131: 109–117.
38.
CalabreseALaiCG (2013) Fragility functions of blockwork wharves using artificial neural networks. Soil Dynamics and Earthquake Engineering52: 88–102.
39.
CarreñoMLCardonaODBarbatAH (2010) Computational tool for post-earthquake evaluation of damage in buildings. Earthquake Spectra26(1): 63–86.
40.
CasciatiFRodellarJYildirimU (2012) Active and semi-active control of structures-theory and applications: A review of recent advances. Journal of Intelligent Material Systems and Structures23(11): 1181–1195.
41.
ChangCRoschkeP (1998) Neural network modeling of a magnetorheological damper. Journal of Intelligent Material Systems and Structures9: 755–764.
42.
ChiouBDarraghRGregorNSilvaW (2008) NGA project strong-motion database. Earthquake Spectra24(1): 23–44.
43.
Chiru-DanzerMJuangCHChristopherRASuberJ (2001) Estimation of liquefaction-induced horizontal displacements using artificial neural networks. Canadian Geotechnical Journal38(1): 200–207.
44.
ConteJPDurraniAJSheltonRO (1994) Seismic response modeling of multi-story buildings using neural networks. Journal of Intelligent Material Systems and Structures5(3): 392–402.
45.
ContentoAGardoniPEgidioADi De LeoAM (2017) Probability models to assess the seismic safety of rigid block-like structures and the effectiveness of two safety devices. Procedia Engineering199: 1164–1169.
46.
CornellCAJalayerFHamburgerROFoutchDA (2002) Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines. Journal of Structural Engineering128: 526–533.
47.
CubrinovskiMWinkleyAHaskellJPalermoAWotherspoonLRobinsonKBradleyBBrabhaharanPHughesM (2014) Spreading-induced damage to short-span bridges in Christchurch, New Zealand. Earthquake Spectra30(1): 57–83.
48.
De FeliceGGianniniR (2010) An efficient approach for seismic fragility assessment with application to old reinforced concrete bridges. Journal of Earthquake Engineering14(2): 231–251.
49.
De GrandisSDomaneschiMPerottiF (2009) A numerical procedure for computing the fragility of NPP components under random seismic excitation. Nuclear Engineering and Design239(11): 2491–2499.
50.
De LautourROOmenzetterP (2009) Prediction of seismic-induced structural damage using artificial neural networks. Engineering Structures31(2): 600–606.
51.
De LautourROOmenzetterP (2010) Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mechanical Systems and Signal Processing24(5): 1556–1569.
52.
De StefanoASabiaDSabiaL (1999) Probabilistic neural networks for seismic damage mechanisms prediction. Earthquake Engineering & Structural Dynamics28(8): 807–821.
53.
DemartinosKDritsosS (2006) First-level pre-earthquake assessment of buildings using fuzzy logic. Earthquake Spectra22(4): 865–885.
54.
DerakhshaniAForuzanAH (2019) Predicting the principal strong ground motion parameters: A deep learning approach. Applied Soft Computing80: 192–201.
55.
DerrasBBardPYCottonF (2014) Towards fully data driven ground-motion prediction models for Europe. Bulletin of Earthquake Engineering12(1): 495–516.
56.
DhanyaJRaghukanthSTG (2018) Ground motion prediction model using artificial neural network. Pure and Applied Geophysics175(3): 1035–1064.
57.
DomingosP (2012) A few useful things to know about machine learning. Communications of the ACM55(10): 78.
58.
DouglasJ (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 Reviews61(1–2): 43–104.
59.
DuAPadgettJE (2019) Multivariate surrogate demand modeling of highway bridge structures. In: 12th Canadian conference on earthquake engineering, Quebec City, Canada, 17–20 June, p. 8.
60.
DukesJMangalathuSPadgettJEDesRochesR (2018) Development of a bridge-specific fragility methodology to improve the seismic resilience of bridges. Earthquake and Structures15(3): 253–261.
EchardBGaytonNLemaireM (2011) AK-MCS: An active learning reliability method combining kriging and Monte Carlo Simulation. Structural Safety33(2): 145–154.
63.
ElwoodECorotisRB (2015) Application of fuzzy pattern recognition of seismic damage to concrete structures. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering1(4): 04015011.
64.
FaghmousJHKumarV (2014) A big data guide to understanding climate change: The case for theory-guided data science. Big Data2(3): 155–163.
65.
FarfaniHABehnamfarFFathollahiA (2015) Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines. Expert Systems with Applications42(22): 8971–8981.
66.
FerrarioEPedroniNZioELopez-CaballeroF (2017) Bootstrapped artificial neural networks for the seismic analysis of structural systems. Structural Safety67: 70–84.
67.
FischerCCTibbettsKJMorganDCederG (2006) Predicting crystal structure by merging data mining with quantum mechanics. Nature Materials5(8): 641–646.
68.
FiscoNRAdeliH (2011) Smart structures: Part I—Active and semi-active control. Scientia Iranica18(3A): 275–284.
69.
FuJLiaoGYuMLiPLaiJ (2016) NARX neural network modeling and robustness analysis of magnetorheological elastomer isolator. Smart Materials and Structures25(12): 1–17.
70.
FullerR (1995) Neural Fuzzy Systems. CBE Life Sciences Education, vol. 9. Turku: Abo Akademi University.
71.
GandomiAHAlaviAHMousaviMTabatabaeiSM (2011) A hybrid computational approach to derive new ground-motion prediction equations. Engineering Applications of Artificial Intelligence24(4): 717–732.
72.
GaoYMosalamKM (2018) Deep transfer learning for image-based structural damage recognition. Computer-Aided Civil and Infrastructure Engineering33(9): 748–768.
73.
GarcíaSRRomoMPBoteroE (2008) A neurofuzzy system to analyze liquefaction-induced lateral spread. Soil Dynamics and Earthquake Engineering28(3): 169–180.
74.
GermanSBrilakisIDesrochesR (2012) Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments. Advanced Engineering Informatics26(4): 846–858.
75.
GermanSJeonJ-SZhuZBearmanCBrilakisIDesRochesRLowesL (2013) Machine vision-enhanced postearthquake inspection. Journal of Computing in Civil Engineering27(6): 622–634.
76.
GhaboussiJJoghataieA (1995) Active control of structures using neural networks. Journal of Engineering Mechanics121(4): 555–567.
77.
GholizadehSSalajeghehJSalajeghehE (2009) An intelligent neural system for predicting structural response subject to earthquakes. Advances in Engineering Software40(8): 630–639.
78.
GhoshJSoodP (2016) Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges. Reliability Engineering and System Safety154: 197–218.
79.
GhoshJPadgettJEDueñas-OsorioL (2013) Surrogate modeling and failure surface visualization for efficient seismic vulnerability assessment of highway bridges. Probabilistic Engineering Mechanics34: 189–199.
80.
GhoshJRokneddinKPadgettJEDueñas-OsorioL (2014) Seismic reliability assessment of aging highway bridge networks with field instrumentation data and correlated failures, I: Methodology. Earthquake Spectra30(2): 795–817.
81.
GhoshSRoyAChakrabortyS (2018) Support vector regression based metamodeling for seismic reliability analysis of structures. Applied Mathematical Modelling64: 584–602.
82.
GidarisITaflanidisAAMavroeidisGP (2015) Kriging metamodeling in seismic risk assessment based on stochastic ground motion models. Earthquake Engineering & Structural Dynamics44(14): 2377–2399.
83.
GodaKTesfamariamS (2015) Multi-variate seismic demand modelling using copulas: Application to non-ductile reinforced concrete frame in Victoria, Canada. Structural Safety56: 39–51.
84.
GohATC (1994) Seismic liquefaction potential assessed by neural networks. Journal of Geotechnical Engineering120(9): 1467–1480.
85.
GohATC (1996) Neural-network modeling of CPT seismic liquefaction data. Journal of Geotechnical Engineering122(1): 70–73.
86.
GohATCGohSH (2007) Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics34(5): 410–421.
87.
GohSHZhangWG (2014) An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines. Engineering Geology170: 1–10.
88.
GongLWangCWuFZhangJZhangHLiQ (2016) Earthquake-induced building damage detection with post-event sub-meter VHR TerraSAR-X staring spotlight imagery. Remote Sensing8(11): 1–21.
89.
GonzálezMPZapicoJL (2008) Seismic damage identification in buildings using neural networks and modal data. Computers and Structures86(3–5): 416–426.
90.
GülkanPKalkanE (2002) Attenuation modeling of recent earthquakes in Turkey. Journal of Seismology6(3): 397–409.
91.
GüllüH (2012) Prediction of peak ground acceleration by genetic expression programming and regression: A comparison using likelihood-based measure. Engineering Geology141: 92–113.
92.
GüllüHErçelebiE (2007) A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey. Engineering Geology93(3–4): 65–81.
93.
Hamze-ZiabariSMBakhshpooriT (2018) Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5′ and CART algorithms. Applied Soft Computing Journal68: 147–161.
94.
HannaAMUralDSaygiliG (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dynamics and Earthquake Engineering27(6): 521–540.
95.
Hariri-ArdebiliMAPourkamali-AnarakiF (2018) Simplified reliability analysis of multi hazard risk in gravity dams via machine learning techniques. Archives of Civil and Mechanical Engineering18(2): 592–610.
96.
HoTK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, Montreal, QC, Canada, 14–16 August, pp. 278–282. New York: IEEE.
97.
HoangN-DBuiDT (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: A multi-dataset study. Bulletin of Engineering Geology and the Environment77(1): 191–204.
98.
HosmerDWLemeshowS (2000) Applied Logistic Regression. 2nd ed. New York: John Wiley.
99.
HousnerGWBergmanLACaugheyTKChassiakosAGClausROMasriSFSkeltonRESoongTTSpencerBFYaoJTP (1997) Structural control: Past, present, and future. Journal of Engineering Mechanics123(9): 897–971.
100.
HuangCSHungSLWenCMTuTT (2003) A neural network approach for structural identification and diagnosis of a building from seismic response data. Earthquake Engineering and Structural Dynamics32(2): 187–206.
101.
HuangGQiuWZhangJ (2017) Modelling seismic fragility of a rock mountain tunnel based on support vector machine. Soil Dynamics and Earthquake Engineering102: 160–171.
102.
HuangHBurtonHV (2019) Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning. Journal of Building Engineering25: 100767.
103.
HungSLHuangCSWenCMHsuYC (2003) Nonparametric identification of a building structure from experimental data using wavelet neural network. Computer-aided Civil and Infrastructure Engineering18(5): 356–368.
104.
HutchinsonTFrankeKArduinoPAsimakiDBarrettBDafniJDashtiSEl MohtarCSHastingsNLemnitzerALoEMartinezAMontalvaGMontgomeryJWoodC (2019) GEER Puebla-Mexico City Earthquake. DesignSafe-CI. DOI: 10.17603/ds2-aetm-8891.
105.
IdrissIMBoulangerRW (2006) Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Soil Dynamics and Earthquake Engineering26: 115–130.
106.
JavadiAARezaniaMNezhadMM (2006) Evaluation of liquefaction induced lateral displacements using genetic programming. Computers and Geotechnics33(4–5): 222–233.
107.
Javan-EmroozHEskandari-GhadiMMirzaeiN (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 America108(4): 2305–2332.
108.
JengC-HMoYL (2004) Quick seismic response estimation of prestressed concrete bridges using artificial neural networks. Journal of Computing in Civil Engineering18(4): 360–372.
109.
JeonJSMangalathuSSongJDesrochesR (2019) Parameterized seismic fragility curves for curved multi-frame concrete box-girder bridges using Bayesian parameter estimation. Journal of Earthquake Engineering23: 954–979.
110.
JeonJ-SShafieezadehADesRochesR (2014) Statistical models for shear strength of RC beam-column joints using machine-learning techniques. Earthquake Engineering & Structural Dynamics43: 2075–2095.
111.
JiangXAdeliH (2005) Dynamic wavelet neural network for nonlinear identification of highrise buildings. Computer-Aided Civil and Infrastructure Engineering20(5): 316–330.
112.
JiangXAdeliH (2007) Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings. International Journal for Numerical Methods in Engineering71: 606–629.
113.
JiangXAdeliH (2008a) Dynamic fuzzy wavelet neuroemulator for non-linear control of irregular building structures. International Journal for Numerical Methods in Engineering74: 1045–1066.
114.
JiangXAdeliH (2008b) Neuro-genetic algorithm for non-linear active control of structures. International Journal for Numerical Methods in Engineering75: 770–786.
115.
JiangXMahadevanS (2008) Bayesian probabilistic inference for nonparametric damage detection of structures. Journal of Engineering Mechanics134(10): 820–831.
116.
JiangXMahadevanSYuanY (2017) Fuzzy stochastic neural network model for structural system identification. Mechanical Systems and Signal Processing82: 394–411.
117.
JoghataieAFarrokhM (2008) Dynamic analysis of nonlinear frames by Prandtl neural networks. Journal of Engineering Mechanics134(11): 961–969.
118.
JolliffeIT (2002) Principal Component Analysis (Springer Series in Statistics). 2nd ed. New York: Springer.
119.
JuangCHChenCJ (1999) CPT-based liquefaction evaluation using artificial neural networks. Computer-Aided Civil and Infrastructure Engineering14(3): 221–229.
120.
KameshwarSPadgettJE (2014) Multi-hazard risk assessment of highway bridges subjected to earthquake and hurricane hazards. Engineering Structures78: 154–166.
121.
KameshwarSPadgettJE (2018) Effect of vehicle bridge interaction on seismic response and fragility of bridges. Earthquake Engineering and Structural Dynamics47(3): 697–713.
122.
KameshwarSVishnuNPadgettJ (2019) Response and Fragility Modeling of Aging Bridges Subjected to Earthquakes and Truck Loads. DesignSafe-CI. DOI: 10.17603/ds2-5tzv-qz91
123.
KarimiIKhajiNAhmadiMTMirzayeeM (2010) System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element-boundary element approach. Engineering Structures32(11): 3583–3591.
124.
KarpatneAAtluriGFaghmousJHSteinbachMBanerjeeAGangulyAShekharSSamatovaNFKumarV (2017) Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering29(10): 2318–2331.
125.
KavehABakhshpooriTHamze-ZiabariSM (2016) Derivation of new equations for prediction of principal ground-motion parameters using M5′ algorithm. Journal of Earthquake Engineering20(6): 910–930.
126.
KayaZ (2016) Predicting liquefaction-induced lateral spreading by using neural network and neuro-fuzzy techniques. International Journal of Geomechanics16(4): 04015095.
127.
KerhTTingSB (2005) Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system. Engineering Applications of Artificial Intelligence18(7): 857–866.
128.
KhalatbarisoltaniASoleymaniMKhodadadiM (2019) Online control of an active seismic system via reinforcement learning. Structural Control and Health Monitoring26(3): 1–18.
129.
KhodabandolehlouHPekcanGFadaliMSSalemMMA (2018) Active neural predictive control of seismically isolated structures. Structural Control and Health Monitoring25(1): 1–15.
130.
KhosravikiaFClaytonPNagyZ (2019) Artificial neural network-based framework for developing ground-motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas. Seismological Research Letters90(2A): 604–613.
131.
KhosravikiaFPotterAPrakhovVZalachorisGChengTTiwariAClaytonPCoxBRathjeEWilliamsonEPaineJFrohlichC (2018) Seismic Vulnerability and Post-event Actions for Texas Bridge Infrastructure. Austin, TX: The University of Texas at Austin.
132.
KiaASensoyS (2014) Classification of earthquake-induced damage for R/C slab column frames using multiclass SVM and its combination with MLP neural network. Mathematical Problems in Engineering2014; 2014: 734072.
133.
KicingerRArciszewskiTDe JongK (2005) Evolutionary computation and structural design: A survey of the state-of-the-art. Computers and Structures83(23–24): 1943–1978.
134.
KimDHLeeIW (2001) Neuro-control of seismically excited steel structure through sensitivity evaluation scheme. Earthquake Engineering and Structural Dynamics30(9): 1361–1377.
135.
KimDHKimDChangSJungHY (2008) Active control strategy of structures based on lattice type probabilistic neural network. Probabilistic Engineering Mechanics23(1): 45–50.
136.
KimJ-TJungH-JLeeI-W (2000) Optimal structural control using neural networks. Journal of Engineering Mechanics126(2): 201–205.
137.
KohestaniVRArdakaniMH (2015) Evaluation of liquefaction potential based on CPT data using random forest. Natural Hazards79(2): 1079–1089.
138.
KongQTrugmanDTRossZEBiancoMJMeadeBJGerstoftP (2018) Machine learning in seismology: Turning data into insights. Seismological Research Letters90(1): 3–14.
139.
KongZTangBDengLLiuWHanY (2020) Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units. Renewable Energy146: 760–768.
140.
KoutsourelakisPS (2010) Assessing structural vulnerability against earthquakes using multi-dimensional fragility surfaces: A Bayesian framework. Probabilistic Engineering Mechanics25(1): 49–60.
141.
KrishnamoorthyABhatSBhasariD (2017) Radial basis function neural network algorithm for semi-active control of base-isolated structures. Structural Control and Health Monitoring24(10): 1–11.
142.
LagarosNDFragiadakisM (2007) Fragility assessment of steel frames using neural networks. Earthquake Spectra23(4): 735–752.
143.
LagarosNDTsompanakisYPsarropoulosPNGeorgopoulosEC (2009) Computationally efficient seismic fragility analysis of geostructures. Computers and Structures87(19–20): 1195–1203.
144.
LattanziDMillerGREberhardMOHaraldssonOS (2015) Bridge column maximum drift estimation via computer vision. Journal of Computing in Civil Engineering30(4): 04015051.
145.
LeCunYBengioYHintonG (2015) Deep learning. Nature521: 436–444.
146.
LeeHJJungHJChoSWLeeIW (2008) An experimental study of semiactive modal neuro-control scheme using MR damper for building structure. Journal of Intelligent Material Systems and Structures19(9): 1005–1015.
147.
LeeHJYangGJungHJSpencerBFLeeIW (2006) Semi-active neurocontrol of a base-isolated benchmark structure. Structural Control and Health Monitoring13(2–3): 682–692.
148.
LiHNYangH (2007) System identification of dynamic structure by the multi-branch BPNN. Neurocomputing70(4–6): 835–841.
149.
LiaoTWEgbeluPJSarkerBRLeuSS (2011) Metaheuristics for project and construction management—A state-of-the-art review. Automation in Construction20(5): 491–505.
150.
LielABHaseltonCBDeierleinGGBakerJW (2009) Incorporating modeling uncertainties in the assessment of seismic collapse risk of buildings. Structural Safety31(2): 197–211.
151.
LiuZTesfamariamS (2012) Prediction of lateral spread displacement: Data-driven approaches. Bulletin of Earthquake Engineering10(5): 1431–1454.
152.
LiuZZhangZ (2018) Artificial neural network based method for seismic fragility analysis of steel frames. KSCE Journal of Civil Engineering22(2): 708–717.
153.
LuPChenSZhengY (2012) Artificial intelligence in civil engineering. Mathematical Problems in Engineering2012: 145974.
154.
LucoNManuelLBaldavaSBazzurroP (2005) Correlation of damage of steel moment-resisting frames to a vector-valued set of ground motion parameters. In: Proceedings of the 9th international conference on structural safety and reliability (ICOSSAR05), Rome, 19–23 June, pp. 2719–2726. Rotterdam: Millpress.
155.
LuoHPaalSG (2018) Machine learning–based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals. Journal of Computing in Civil Engineering32(5): 04018042.
156.
LuoHPaalSG (2019) A locally weighted machine learning model for generalized prediction of drift capacity in seismic vulnerability assessments. Computer-Aided Civil and Infrastructure Engineering34: 935–950.
157.
MadanA (2005) Vibration control of building structures using self-organizing and self-learning neural networks. Journal of Sound and Vibration287(4–5): 759–784.
158.
MahmoudiSNChouinardL (2016) Seismic fragility assessment of highway bridges using support vector machines. Bulletin of Earthquake Engineering14(6): 1571–1587.
159.
MangalathuSBurtonHV (2019) Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions. International Journal of Disaster Risk Reduction36: 101111.
160.
MangalathuSJeonJS (2018) Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Engineering Structures160: 85–94.
161.
MangalathuSJeonJ-S (2019a) Machine learning–based failure mode recognition of circular reinforced concrete bridge columns: Comparative study. Journal of Structural Engineering145(10): 04019104.
162.
MangalathuSJeonJ-S (2019b) Stripe-based fragility analysis of concrete bridge classes using machine learning techniques. Earthquake Engineering & Structural Dynamics48: 1238–1255.
163.
MangalathuSHeoGJeonJS (2018a) Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes. Engineering Structures162: 166–176.
164.
MangalathuSJeonJSDesRochesR (2018b) Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression. Earthquake Engineering and Structural Dynamics47(3): 784–801.
165.
MarkičŠStankovskiV (2013) An equation-discovery approach to earthquake-ground-motion prediction. Engineering Applications of Artificial Intelligence26(4): 1339–1347.
166.
MemarzadehMPozziM (2019) Model-free reinforcement learning with model-based safe exploration: Optimizing adaptive recovery process of infrastructure systems. Structural Safety80: 46–55.
167.
MitraNMitraSLowesLN (2011) Probabilistic model for failure initiation of reinforced concrete interior beam-column connections subjected to seismic loading. Engineering Structures33(1): 154–162.
168.
MitropoulouCCPapadrakakisM (2011) Developing fragility curves based on neural network IDA predictions. Engineering Structures33(12): 3409–3421.
169.
MohammadnejadAKMousaviSMTorabiMMousaviMAlaviAH (2012) Robust attenuation relations for peak time-domain parameters of strong ground motions. Environmental Earth Sciences67(1): 53–70.
MorfidisKKostinakisK (2017) Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks. Advances in Engineering Software106: 1–16.
172.
MorfidisKKostinakisK (2018) Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks. Engineering Structures165: 120–141.
173.
MosalamKAbucharVArchboldJArtetaCFischerEGunaySHakhamaneshiMHassanWMicheliLMuinSPajaroMiranda CPretellDuctram ARPengHRobertsonIRouecheDZiotopoulouK (2019) Steer—M6.4 and M7.1 Ridgecrest, CA Earthquakes on July 4-5, 2019: Preliminary Virtual Reconnaissance Report (PVRR). DesignSafe-CI. DOI: 10.17603/ds2-xqfh-1631.
174.
MossRESeedRBKayenREStewartJPDer KiureghianACetinKO (2006) CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential. Journal of Geotechnical and Geoenvironmental Engineering132(8): 1032–1051.
175.
MurphyKP (2012) Machine Learning: A Probabilistic Perspective. Chance Encounters: Probability in Education. Cambridge, MA; London: The MIT Press.
176.
NaderpourHMirrashidM (2019) Shear failure capacity prediction of concrete beam–column joints in terms of ANFIS and GMDH. Practice Periodical on Structural Design and Construction24(2): 04019006.
177.
NakamuraMMasriSFChassiakosAGCauGheyTK (1998) A method for non-parametric damage detection through the use of neural networks. Earthquake Engineering and Structural Dynamics27: 997–1010.
178.
NguyenKTPMedjaherK (2019) A new dynamic predictive maintenance framework using deep learning for failure prognostics. Reliability Engineering and System Safety188: 251–262.
179.
OommenTBaiseLG (2010) Model development and validation for intelligent data collection for lateral spread displacements. Journal of Computing in Civil Engineering24(6): 467–477.
180.
PaalSGJeonJ-SBrilakisIDesRochesR (2014) Automated damage index estimation of reinforced concrete columns for post-earthquake evaluations. Journal of Structural Engineering141(9): 04014228.
181.
PalM (2006) Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics30(10): 983–996.
182.
PanYAgrawalAKGhosnM (2007) Seismic fragility of continuous steel highway bridges in New York State. Journal of Bridge Engineering12(6): 689–699.
183.
PangYDangXYuanW (2014) An artificial neural network based method for seismic fragility analysis of highway bridges. Advances in Structural Engineering17(3): 413–428.
184.
ParkJTowashirapornP (2014) Rapid seismic damage assessment of railway bridges using the response-surface statistical model. Structural Safety47: 1–12.
185.
PerlovskyL (2000) Neural Networks and Intellect: Using Model-Based Concepts. 1st ed. Oxford: Oxford University Press.
186.
PerottiFDomaneschiMDe GrandisS (2013) The numerical computation of seismic fragility of base-isolated nuclear power plants buildings. Nuclear Engineering and Design262: 189–200.
187.
Peyk-HerfehMShahbahramiA (2014) Evaluation of adaptive boosting and neural network in earthquake damage levels detection. International Journal of Computer Applications100(3): 23–29.
188.
PirhadiNTangXYangQKangF (2018) A new equation to evaluate liquefaction triggering using the response surface method and parametric sensitivity analysis. Sustainability11(1): 1–24.
189.
PorterKA (2003) An overview of PEER’s performance-based earthquake engineering methodology. In: Ninth international conference on applications of statistics and probability in civil engineering, San Francisco, CA, 6–9 July, pp. 1–8.
190.
RaissiMKarniadakisGE (2018) Hidden physics models: Machine learning of nonlinear partial differential equations. Journal of Computational Physics357: 125–141.
191.
RajeevPTesfamariamS (2012) Seismic fragilities for reinforced concrete buildings with consideration of irregularities. Structural Safety39: 1–13.
192.
RamakrishnanDSinghTNPurwarNBardeKSGulatiAGuptaS (2008) Artificial neural network and liquefaction susceptibility assessment: A case study using the 2001 Bhuj earthquake data, Gujarat, India. Computational Geosciences12(4): 491–501.
193.
RaoMMDattaTK (2006) Modal seismic control of building frames by artificial neural network. Journal of Computing in Civil Engineering20(1): 69–73.
194.
RathjeEMDawsonCPadgettJEPinelliJ-PStanzioneDAdairAArduinoPBrandenbergSJCockerillTDeyCEstevaMHaanFLJrHanlonMKareemALowesLMockSMosquedaG (2017) DesignSafe: New cyberinfrastructure for natural hazards engineering. Natural Hazards Review18(3): 06017001.
195.
RaviKiran AReddyGRAgrawalMK (2019) Seismic fragility analysis of pressurized piping systems considering ratcheting: A case study. International Journal of Pressure Vessels and Piping169(412): 26–36.
196.
RawlingsJOPantulaSGDickeyDA (1998) Applied Regression Analysis: A Research Tool (Springer Texts in Statistics). 2nd ed. New York: Springer.
197.
RezaniaMFaramarziAJavadiAA (2011) An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement. Engineering Applications of Artificial Intelligence24(1): 142–153.
198.
RezaniaMJavadiAAGiustolisiO (2010) Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression. Computers and Geotechnics37(1–2): 82–92.
199.
RicciPDe RisiMTVerderameGMManfrediG (2013) Influence of infill distribution and design typology on seismic performance of low- and mid-rise RC buildings. Bulletin of Earthquake Engineering11(5): 1585–1616.
200.
SahaSKMatsagarVChakrabortyS (2016) Uncertainty quantification and seismic fragility of base-isolated liquid storage tanks using response surface models. Probabilistic Engineering Mechanics43: 20–35.
201.
SahooDMChakravertyS (2018) Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data. IEEE Transactions on Systems, Man, and Cybernetics: Systems48(1): 1–10.
202.
SahuDNishanthMDhirPKSarkarPDavisRMangalathuS (2019) Stochastic response of reinforced concrete buildings using high dimensional model representation. Engineering Structures179: 412–422.
203.
SakaMPGeemZW (2013) Mathematical and metaheuristic applications in design optimization of steel frame structures: An extensive review. Mathematical Problems in Engineering2013: 271031.
SamuelAL (1959) Some studies in machine learning using the game of checkers. IBM Journal of Research and Development3(3): 535–554.
206.
SchöbiRSudretBMarelliS (2017) Rare event estimation using polynomial-chaos kriging. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering3(2): 1–12.
207.
SenDAghazadehAMousaviANagarajaiahSBaraniukRDabakA (2019) Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes. Mechanical Systems and Signal Processing131: 524–537.
208.
SeoJLinzellDG (2010) Probabilistic vulnerability scenarios for horizontally curved steel I-girder bridges under earthquake loads. Transportation Research Record: Journal of the Transportation Research Board2202: 206–211.
SeoJLinzellDG (2013) Use of response surface metamodels to generate system level fragilities for existing curved steel bridges. Engineering Structures52: 642–653.
211.
SeoJParkH (2017) Probabilistic seismic restoration cost estimation for transportation infrastructure portfolios with an emphasis on curved steel I-girder bridges. Structural Safety65: 27–34.
212.
SeoJDueñas-OsorioLCraigJIGoodnoBJ (2012) Metamodel-based regional vulnerability estimate of irregular steel moment-frame structures subjected to earthquake events. Engineering Structures45: 585–597.
213.
ShahinMA (2014) A review of artificial intelligence applications in shallow foundations. International Journal of Geotechnical Engineering9(1): 49–60.
214.
ShahinMA (2016) State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers7(1): 33–44.
SilvaMSGarciaL (2001) Earthquake damage assessment based on fuzzy logic and neural networks. Earthquake Spectra17(1): 89–112.
217.
StewartJPKramerSLKwakDYGreenfieldMWKayenRETokimatsuKBrayJDBeyzaeiCZCubrinovskiMSekiguchiTNakaiSBozorgniaY (2016) PEER-NGL project: Open source global database and model development for the next-generation of liquefaction assessment procedures. Soil Dynamics and Earthquake Engineering91: 317–328.
218.
SubasriRSureshSNatarajanAM (2014) Discrete direct adaptive ELM controller for active vibration control of nonlinear base isolation buildings. Neurocomputing129: 246–256.
219.
SureshSNarasimhanSNagarajaiahSSundararajanN (2010) Fault-tolerant adaptive control of nonlinear base-isolated buildings using EMRAN. Engineering Structures32(8): 2477–2487.
220.
TezcanJChengQ (2012) Support vector regression for estimating earthquake response spectra. Bulletin of Earthquake Engineering10(4): 1205–1219.
221.
ThomasSPillaiGNPalK (2017) Prediction of peak ground acceleration using ϵ-SVR, ν-SVR and Ls-SVR algorithm. Geomatics, Natural Hazards and Risk8(2): 177–193.
222.
ThomasSPillaiGNPalKJagtapP (2016) Prediction of ground motion parameters using randomized ANFIS (RANFIS). Applied Soft Computing Journal40: 624–634.
223.
TibshiraniR (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological)58(1): 267–288.
224.
TrugmanDTShearerPM (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 America108(2): 929–945.
225.
TsompanakisYLagarosNDPsarropoulosPNGeorgopoulosEC (2009) Simulating the seismic response of embankments via artificial neural networks. Advances in Engineering Software40(8): 640–651.
226.
ÜlgenDEnginHK (2007) A study of CPT based liquefaction assessment using artificial neural networks. In: 4th international conference on earthquake geotechnical engineering, Thessaloniki, 25–28 June, paper no. 1219, pp. 1–12. New York: Springer.
227.
VafaeiMAdnanABRahmanABA (2013) Real-time seismic damage detection of concrete shear walls using artificial neural networks. Journal of Earthquake Engineering17(1): 137–154.
228.
VafaeiMAdnanABRahmanABA (2014) A neuro-wavelet technique for seismic damage identification of cantilever structures. Structure and Infrastructure Engineering10(12): 1666–1684.
229.
VapnikV (1998) Statistical Learning Theory. 1st ed. New York: John Wiley & Sons.
230.
VerderameGMRicciPDe LucaFDel GaudioCDe RisiMT (2014) Damage scenarios for RC buildings during the 2012 Emilia (Italy) earthquake. Soil Dynamics and Earthquake Engineering66: 385–400.
231.
WagnerNRondinelliJM (2016) Theory-guided machine learning in materials science. Frontiers in Materials3: 1–9.
232.
WallenRD (2004) The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance. Biomedical Instrumentation and Technology, vol. 38. 2nd ed. Boca Raton, FL; London; New York: CRC Press.
233.
WangJRahmanMS (1999) A neural network model for liquefaction-induced horizontal ground displacement. Soil Dynamics and Earthquake Engineering18(8): 555–568.
234.
WangZPedroniNZentnerIZioE (2018) Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment. Engineering Structures162: 213–225.
235.
WenYKGhaboussiJVeniniPNikzadK (1995) Control of structures using neural networks. Smart Materials and Structures4(1A): 149–157.
236.
WhitleyDStarkweatherTBogartC (1990) Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing14(3): 347–361.
237.
WuZXuBYokoyamaK (2002) Decentralized parametric damage detection based on neural networks. Computer-Aided Civil and Infrastructure Engineering17(3): 175–184.
238.
XiaPQ (2003) An inverse model of MR damper using optimal neural network and system identification. Journal of Sound and Vibration266(5): 1009–1023.
239.
XieYDesRochesR (2019) Sensitivity of seismic demands and fragility estimates of a typical California highway bridge to uncertainties in its soil-structure interaction modeling. Engineering Structures189: 605–617.
240.
XieYZhangJ (2016) Optimal design of seismic protective devices for highway bridges using performance-based methodology and multiobjective genetic optimization. Journal of Bridge Engineering22(3): 04016129.
241.
XieYZhangJ (2018) Design and optimization of seismic isolation and damping devices for highway bridges based on probabilistic repair cost ratio. Journal of Structural Engineering144(8): 04018125.
242.
XieYZhangJDesRochesRPadgettJE (2019a) Seismic fragilities of single-column highway bridges with rocking column-footing. Earthquake Engineering & Structural Dynamics48: 843–864.
243.
XieYZhengQYangCSWZhangWDesRochesRPadgettJETacirogluE (2019b) Probabilistic models of abutment backfills for regional seismic assessment of highway bridges in California. Engineering Structures180: 452–467.
244.
XuBWuZChenGYokoyamaK (2004) Direct identification of structural parameters from dynamic responses with neural networks. Engineering Applications of Artificial Intelligence17(8): 931–943.
245.
XuBWuZYokoyamaKHaradaTChenG (2005) A soft post-earthquake damage identification methodology using vibration time series. Smart Materials and Structures14(3): 116–124.
246.
XuZDGuoYQ (2008) Neuro-fuzzy control strategy for earthquake-excited nonlinear magnetorheological structures. Soil Dynamics and Earthquake Engineering28(9): 717–727.
247.
XuZDShenYPGuoYQ (2003) Semi-active control of structures incorporated with magnetorheological dampers using neutral networks. Smart Materials and Structures12(1): 80–87.
248.
YakutOAlliH (2011) Neural based sliding-mode control with moving sliding surface for the seismic isolation of structures. Journal of Vibration and Control17(14): 2103–2116.
249.
YangYDornCManciniTTalkenZNagarajaiahSKenyonGFarrarCMascareñasD (2017) Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements. Journal of Sound and Vibration390: 232–256.
250.
YaseenZMAfanHATranMT (2018) Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm. IOP Conference Series: Earth and Environmental Science143(1): 1–7.
Yerlikaya-OzkurtFAskanAWeberG-W (2014) An alternative approach to the ground motion prediction problem by a non-parametric adaptive regression method. Engineering Optimization46(12): 1651–1668.
253.
YoudTL (2018) Application of MLR procedure for prediction of liquefaction-induced lateral spread displacement. Journal of Geotechnical and Geoenvironmental Engineering144(6): 04018033.
254.
YoudTLHansenCMBartlettSF (2002) Revised multilinear regression equations for prediction of lateral spread displacement. Journal of Geotechnical and Geoenvironmental Engineering128(12): 1007–1017.
255.
YunHBTasbighooFMasriSFCaffreyJPWolfeRWMakrisNBlackC (2008) Comparison of modeling approaches for full-scale nonlinear viscous dampers. JVC/Journal of Vibration and Control14(1–2): 51–76.
256.
ZhangJSatoTIaiSHutchinsonT (2008) A pattern recognition technique for structural identification using observed vibration signals: Linear case studies. Engineering Structures30(5): 1439–1446.
257.
ZhangJXieYWuG (2019a) Seismic responses of bridges with rocking column-foundation: A dimensionless regression analysis. Earthquake Engineering and Structural Dynamics48(1): 152–170.
258.
ZhangJZhangLMHuangHW (2013) Evaluation of generalized linear models for soil liquefaction probability prediction. Environmental Earth Sciences68(7): 1925–1933.
259.
ZhangRChenZChenSZhengJBüyüköztürkOSunH (2019b) Deep long short-term memory networks for nonlinear structural seismic response prediction. Computers and Structures220: 55–68.
260.
ZhangYBurtonHV (2019) Pattern recognition approach to assess the residual structural capacity of damaged tall buildings. Structural Safety78: 12–22.
261.
ZhangYBurtonHVSunHShokrabadiM (2018) A machine learning framework for assessing post-earthquake structural safety. Structural Safety72: 1–16.
262.
ZhouJLiEWangMChenXShiXJiangL (2019) Feasibility of stochastic gradient boosting approach for evaluating seismic liquefaction potential based on SPT and CPT case histories. Journal of Performance of Constructed Facilities33(3): 04019024.
263.
ZouHHastieT (2005) Regression and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology)67(2): 301–320.
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