Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques

Assessing the behavior of earth dams under dynamic loads is one of the most significant problems with the design of such large structures. The purpose of this study is to provide new models for predicting dam dispersion in real earthquake conditions. In the first phase, 103 real cases of deformation in earth dams were collected and analyzed due to earthquakes that occurred over recent years. Using nonlinear and machine learning techniques, i.e., group method of data handling (GMDH) and multivariate adaptive regression splines (MARS), two models for prediction of the slope deformation in earth dams under the various types of earthquakes were applied and developed. The main parameters used in these simulation techniques were earthquake magnitude (Mw), fundamental period ratio (Td/Tp), yield acceleration ratio (ay/amax) as inputs and value of slope deformation (Dave) as output. Finally, in order to check the accuracy of the results of the new models, a comparison was made with the previous relations and models in seismic conditions for the slope deformation in earth dams. The results showed that the MARS model, which is able to provide a mathematical equation, has a better result than the GMDH model. These new models are recommended to be used for future analyses based on their flexible capabilities.

[1]  Danial Jahed Armaghani,et al.  Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm , 2020, Engineering with Computers.

[2]  Danial Jahed Armaghani,et al.  Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques , 2020 .

[3]  Binh Thai Pham,et al.  A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles , 2020, Engineering with Computers.

[4]  D. Jahed Armaghani,et al.  Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites , 2020, Natural Resources Research.

[5]  Jian Zhou,et al.  Deep neural network and whale optimization algorithm to assess flyrock induced by blasting , 2021, Engineering with Computers.

[6]  Mahdi Hasanipanah,et al.  Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm , 2020, Engineering with Computers.

[7]  Danial Jahed Armaghani,et al.  The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production , 2020, Engineering with Computers.

[8]  Panagiotis G. Asteris,et al.  Concrete compressive strength using artificial neural networks , 2019, Neural Computing and Applications.

[9]  Jian Zhou,et al.  Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques , 2019, Bulletin of Engineering Geology and the Environment.

[10]  Panagiotis G. Asteris,et al.  Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength , 2019, Applied Sciences.

[11]  Bhatawdekar Ramesh Murlidhar,et al.  A new hybrid method for predicting ripping production in different weathering zones through in situ tests , 2019 .

[12]  Mohammadreza Koopialipoor,et al.  Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples , 2019, Engineering with Computers.

[13]  Jian Zhou,et al.  Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models , 2019, Engineering with Computers.

[14]  P. G. Asteris,et al.  Application of artificial neural networks for the predictionof the compressive strength of cement-based mortars , 2019 .

[15]  Jian Zhou,et al.  Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories , 2019, Safety Science.

[16]  Panagiotis G. Asteris,et al.  Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate , 2019, Applied Sciences.

[17]  Panagiotis G. Asteris,et al.  Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures , 2019, Neural Computing and Applications.

[18]  Haiqing Yang,et al.  Effect of Water Content on Argillization of Mudstone During the Tunnelling process , 2019, Rock Mechanics and Rock Engineering.

[19]  Danial Jahed Armaghani,et al.  A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets , 2019, Engineering with Computers.

[20]  Hoang Nguyen,et al.  A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET , 2019, Engineering with Computers.

[21]  A. Marto,et al.  Application of several optimization techniques for estimating TBM advance rate in granitic rocks , 2019, Journal of Rock Mechanics and Geotechnical Engineering.

[22]  Jian Zhou,et al.  Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance , 2019, Natural Resources Research.

[23]  Liborio Cavaleri,et al.  Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks , 2019, Applied Sciences.

[24]  Danial Jahed Armaghani,et al.  Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions , 2019, Soft Comput..

[25]  Panagiotis G. Asteris,et al.  Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns , 2019, Engineering with Computers.

[26]  Mohammadreza Koopialipoor,et al.  A new approach for estimation of rock brittleness based on non-destructive tests , 2019, Nondestructive Testing and Evaluation.

[27]  Ahmadreza Hedayat,et al.  Application of deep neural networks in predicting the penetration rate of tunnel boring machines , 2019, Bulletin of Engineering Geology and the Environment.

[28]  Hoang Nguyen,et al.  Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile , 2019, Engineering with Computers.

[29]  Jian Zhou,et al.  Optimal Charge Scheme Calculation for Multiring Blasting Using Modified Harries Mathematical Model , 2019, Journal of Performance of Constructed Facilities.

[30]  Hui Chen,et al.  Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models , 2019, Applied Sciences.

[31]  Jian Zhou,et al.  A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network , 2019, Engineering with Computers.

[32]  Ebrahim Noroozi Ghaleini,et al.  Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN , 2019, Environmental Earth Sciences.

[33]  Manoj Khandelwal,et al.  Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques , 2019, Engineering with Computers.

[34]  Amin Mohebkhah,et al.  Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects , 2019, Applied Sciences.

[35]  Danial Jahed Armaghani,et al.  The use of new intelligent techniques in designing retaining walls , 2019, Engineering with Computers.

[36]  Danial Jahed Armaghani,et al.  Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance , 2019, Engineering with Computers.

[37]  Liborio Cavaleri,et al.  Masonry Compressive Strength Prediction Using Artificial Neural Networks , 2018, TMM_CH.

[38]  Panagiotis G. Asteris,et al.  Soft computing-based techniques for concrete beams shear strength , 2019, Procedia Structural Integrity.

[39]  P. G. Asteris,et al.  Compressive strength of natural hydraulic lime mortars using soft computing techniques , 2019, Procedia Structural Integrity.

[40]  Danial Jahed Armaghani,et al.  Intelligent design of retaining wall structures under dynamic conditions , 2019 .

[41]  Danial Jahed Armaghani,et al.  Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique , 2019, IEEE Access.

[42]  Hani S. Mitri,et al.  Evaluation method of rockburst: State-of-the-art literature review , 2018, Tunnelling and Underground Space Technology.

[43]  Haiqing Yang,et al.  Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass , 2018, Tunnelling and Underground Space Technology.

[44]  Edy Tonnizam Mohamad,et al.  Overbreak prediction and optimization in tunnel using neural network and bee colony techniques , 2018, Engineering with Computers.

[45]  M. Nilashi,et al.  Rock tensile strength prediction using empirical and soft computing approaches , 2018, Bulletin of Engineering Geology and the Environment.

[46]  Edy Tonnizam Mohamad,et al.  Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques , 2018, Engineering with Computers.

[47]  Aminaton Marto,et al.  Predicting tunnel boring machine performance through a new model based on the group method of data handling , 2018, Bulletin of Engineering Geology and the Environment.

[48]  Haiqing Yang,et al.  Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides , 2018 .

[49]  Danial Jahed Armaghani,et al.  Three hybrid intelligent models in estimating flyrock distance resulting from blasting , 2018, Engineering with Computers.

[50]  Haiqing Yang,et al.  Investigation on the Cracking Character of Jointed Rock Mass Beneath TBM Disc Cutter , 2018, Rock Mechanics and Rock Engineering.

[51]  Danial Jahed Armaghani,et al.  A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels , 2019, Bulletin of Engineering Geology and the Environment.

[52]  Liborio Cavaleri,et al.  Modeling of surface roughness in electro-discharge machining using artificial neural networks , 2017 .

[53]  Panagiotis G. Asteris,et al.  Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials , 2017, Sensors.

[54]  Danial Jahed Armaghani,et al.  Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition , 2017 .

[55]  Hani S. Mitri,et al.  Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods , 2016, J. Comput. Civ. Eng..

[56]  Jian Zhou,et al.  Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines , 2016 .

[57]  Y. Jafarian,et al.  Simplified Procedure for Coupled Seismic Sliding Movement of Slopes Using Displacement-Based Critical Acceleration , 2016 .

[58]  Gints Jekabsons,et al.  Adaptive Regression Splines toolbox for Matlab/Octave , 2015 .

[59]  Danial Jahed Armaghani,et al.  Application of artificial neural network for predicting shaft and tip resistances of concrete piles , 2015 .

[60]  Aminaton Marto,et al.  Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization , 2014, Arabian Journal of Geosciences.

[61]  Farshid Vahedifard,et al.  Evaluation of simplified methods for predicting earthquake-induced slope displacements in earth dams and embankments , 2013 .

[62]  Jian Zhou,et al.  Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction , 2012 .

[63]  Joseph Wartman,et al.  Uncertainty in seismic slope deformation model predictions , 2011 .

[64]  G. Saygili,et al.  Real-time mapping of earthquake-induced landslides , 2011 .

[65]  S. Rampello,et al.  Evaluation of the seismic response of a homogeneous earth dam , 2009 .

[66]  A. Cavallaro,et al.  Site Response Analysis of the Monte Po Hill in the City of Catania , 2008 .

[67]  Haydar Arslan,et al.  Earthquake induced deformation of earth dams , 2008 .

[68]  Gokhan Saygili,et al.  Empirical Predictive Models for Earthquake-Induced Sliding Displacements of Slopes , 2008 .

[69]  R. Jibson.  Regression models for estimating coseismic landslide displacement , 2007 .

[70]  Jonathan D. Bray,et al.  Simplified Procedure for Estimating Earthquake-Induced Deviatoric Slope Displacements , 2007 .

[71]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[72]  Murat Dicleli,et al.  Predicting the shear strength of reinforced concrete beams using artificial neural networks , 2004 .

[73]  Ellis L. Krinitzsky,et al.  The Bhuj, India, earthquake: lessons learned for earthquake safety of dams on alluvium , 2002 .

[74]  Jonathan D. Bray,et al.  NONLINEAR COUPLED SEISMIC SLIDING ANALYSIS OF EARTH STRUCTURES , 2000 .

[75]  Steven L. Kramer,et al.  MODIFIED NEWMARK MODEL FOR SEISMIC DISPLACEMENTS OF COMPLIANT SLOPES , 1997 .

[76]  Jean-Pierre Bardet,et al.  PERFORMANCE OF SAN FERNANDO DAMS DURING 1994 NORTHRIDGE EARTHQUAKE , 1996 .

[77]  石原 研而,et al.  Soil behaviour in earthquake geotechnics , 1996 .

[78]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[79]  M. K. Yegian,et al.  Earthquake‐Induced Permanent Deformations: Probabilistic Approach , 1991 .

[80]  R. Scott,et al.  LA VILLITA DAM RESPONSE DURING FIVE EARTHQUAKES INCLUDING PERMANENT DEFORMATION , 1990 .

[81]  Raymond B. Seed,et al.  ANALYSES OF DAM FAILURES IN 1985 CHILEAN EARTHQUAKE , 1988 .

[82]  N. N. Ambraseys,et al.  Earthquake‐induced ground displacements , 1988 .

[83]  Shen Choggang.  Some Experiences from Damages of Embankments During Strong Earthquakes China , 1988 .

[84]  A. M. Abdel-Ghaffar,et al.  Nonlinear Dynamic Analyses of an Earth Dam , 1985 .

[85]  Luis Arrau,et al.  Performance of Cogoti Dam Under Seismic Loading , 1985 .

[86]  A. M. Abdel-Ghaffar,et al.  Analysis of Earth Dam Response to Earthquakes , 1979 .

[87]  H. Bolton Seed,et al.  Closure of "Simplified Procedure for Estimating Dam and Embankment Earthquake-Induced Deformations" , 1977 .

[88]  S. K. Sarma.  Seismic stability of earth dams and embankments , 1975 .

[89]  N. Newmark.  Effects of Earthquakes on Dams and Embankments , 1965 .