Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model

The squat reinforced concrete (RC) shear wall having low aspect ratio is a crucial structural component for both conventional buildings and nuclear-related structures due to the substantial role in resisting the lateral seismic loading. The prediction model for shear capacity of these walls becomes essential in ensuring the seismic safety of the building. Therefore, a model to predict the shear strength of squat RC walls has been proposed using a hybrid intelligence algorithm including the artificial neural network and particle swarm optimization algorithm (ANN–PSO). A total of 139 test results of squat walls are collected and utilized to train and test the hybrid ANN–PSO model. The performance of the proposed model has been assessed against the other shear strength models. The proposed model demonstrates good prediction capability with high accuracy for predicting shear strength of the RC walls.

[1]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[2]  C. Ledezma,et al.  Seismic Behavior of Squat Reinforced Concrete Shear Walls , 2002 .

[3]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[4]  Imad A. Basheer,et al.  Selection of Methodology for Neural Network Modeling of Constitutive Hystereses Behavior of Soils , 2000 .

[5]  Sholom M. Weiss,et al.  Heuristic configuration of single hidden-layer feed-forward neural networks , 1992, Applied Intelligence.

[6]  Mahdi Hasanipanah,et al.  Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling , 2016, Engineering with Computers.

[7]  Mohsen Hajihassani,et al.  The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization , 2013, Arabian Journal of Geosciences.

[8]  Andrew S. Whittaker,et al.  Shear strength of squat rectangular reinforced concrete walls , 2008 .

[9]  Michael P. Collins,et al.  Development of the 2004 Canadian Standards Association (CSA) A23.3 shear provisions for reinforced concrete , 2006 .

[10]  Sung-Bae Cho,et al.  A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN , 2010, Neural Computing and Applications.

[11]  Yin Hai-peng.  Research on Seismic Performance of Low-rise Recycled Concrete Shear Walls , 2011 .

[12]  Michael D. Kotsovos,et al.  Behavior of Reinforced Concrete Structural Walls: Strength, Deformation Characteristics, and Failure Mechanism , 1990 .

[13]  Sharon L. Wood,et al.  Shear Strength of Low-Rise Reinforced Concrete Walls , 1990 .

[14]  Cevdet Kerem Gulec,et al.  Performance-based assessment and design of squat reinforced concrete shear walls , 2009 .

[15]  J Schlaich,et al.  TOWARD A CONSISTENT DESIGN OF STRUCTURAL CONCRETE , 1987 .

[16]  Randall W. Poston,et al.  The Framework of the 2014 American Concrete Institute (ACI) 318 Structural Concrete Building Code , 2012 .

[17]  Robert F. Mast,et al.  Auxiliary Reinforcement in Concrete Connections , 1968 .

[18]  Pierino Lestuzzi,et al.  Static-Cyclic Tests on Reinforced Concrete Shear Walls with Low Reinforcement Ratios , 2005 .

[19]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[20]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[21]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[22]  Paulo Cortez,et al.  Particle swarms for feedforward neural network training , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[23]  Carlos Zavala,et al.  Cyclic behavior of thin RC Peruvian shear walls: Full-scale experimental investigation and numerical simulation , 2013 .

[24]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[25]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[26]  Chun Man Chan,et al.  Prediction of Stiffness of Reinforced Concrete Shearwalls under Service Loads , 1999 .

[27]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.

[28]  Xu Wang,et al.  Back analysis for mechanical parameters of surrounding rock for underground roadways based on new neural network , 2017, Engineering with Computers.

[29]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[30]  Xiao Shiyun.  Experimental study on seismic behavior of shear wall with concealed bracings , 2007 .

[31]  S. Chithra,et al.  A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks , 2016 .

[32]  Mário Lopes.  Experimental shear-dominated response of RC walls: Part I: Objectives, methodology and results , 2001 .

[33]  Gustavo J. Parra-Montesinos,et al.  Experimental Study on the Seismic Behavior of High- Performance Fiber-Reinforced Concrete Low-Rise Walls , 2013 .

[34]  M. Grujicic,et al.  Modeling of ballistic-failure mechanisms in gas metal arc welds of mil a46100 armor-grade steel , 2015 .

[35]  Faezehossadat Khademi,et al.  Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression , 2016 .

[36]  Li Hongnan,et al.  Experimental study on seismic restoring performance of reinforced concrete shear walls , 2004 .

[37]  Kypros Pilakoutas.  Earthquake resistant design of reinforced concrete walls , 1990 .

[38]  A. Kappos,et al.  Cyclic Load Behavior of Low-Slenderness Reinforced Concrete Walls: Failure Modes, Strength and Deformation Analysis, and Design Implications , 2000 .

[39]  F. Mazzolani,et al.  Design of Steel Structures for Buildings in Seismic Areas: Eurocode 8: Design of structures for earthquake resistance. Part 1-1 - General rules, seismic actions and rules for buildings , 2017 .

[40]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[41]  Yan Shi.  Seismic Experiments for High Strength Concrete Shear Walls Reinforced with High Strength Rebars , 2010 .