Application of Support Vector Machine and Finite Element Method to predict the mechanical properties of concrete

Compressive strength and Young’s modulus are the main properties used in the design of concrete structures. They are responsible for the cost, safety and dimensioning of the structure, and are generally measured in expensive and time-demanding tests. This fact encourages researches for fast and cost-effective methods to investigate the concrete’s properties. Among the concrete types, structural Lightweight Aggregate Concrete (LWAC) is one of the most employed worldwide, but it presents limited studies and mix design techniques. Thus, this work evaluates and compares the performances of two methods to predict the compressive strength of LWAC samples: Support Vector Machine and Finite Element Method. To this end, both strategies use the LWAC’s mix proportions and the Young’s modulus, and the compressive strength of mortars and aggregates obtained from an experimental program from the literature. The results encourage further researches towards the development of a numerical tool that may assist engineers for practical purposes, since both methods show good agreement with the validation data.

[1]  Manish A. Kewalramani,et al.  Prediction of Concrete Strength Using Neural-Expert System , 2006 .

[2]  Xin Wang,et al.  Grid search as applied to the determination of Mark–Houwink parameters , 2000 .

[3]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[4]  Y. Ke,et al.  Micro-stress analysis and identification of lightweight aggregate’s failure strength by micromechanical modeling , 2014 .

[5]  Caijun Shi,et al.  Prediction of elastic modulus of normal and high strength concrete by support vector machine , 2010 .

[6]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[7]  P. K. Mehta,et al.  Concrete: Microstructure, Properties, and Materials , 2005 .

[8]  Antonio José Tenza-Abril,et al.  Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity , 2018, Construction and Building Materials.

[9]  D. Stephan,et al.  The influence of different concrete additions on the properties of lightweight concrete evaluated using experimental and numerical approaches , 2018, Construction and Building Materials.

[10]  A. Nazari,et al.  Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine , 2015 .

[11]  Jui-Sheng Chou,et al.  Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques , 2011, J. Comput. Civ. Eng..

[12]  Min-Yuan Cheng,et al.  High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model , 2012 .

[13]  J. Mier,et al.  Simple lattice model for numerical simulation of fracture of concrete materials and structures , 1992 .

[14]  Ch Best,et al.  Significance of Tests and Properties of Concrete and Concrete-Making Materials , 1978 .

[15]  Sunday O. Olatunji,et al.  Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete , 2014 .

[16]  Sun-Myung Kim,et al.  Computational applications of a coupled plasticity-damage constitutive model for simulating plain concrete fracture , 2010 .

[17]  Abbas M. Abd,et al.  Modelling the strength of lightweight foamed concrete using support vector machine (SVM) , 2017 .

[18]  Ali Sadrmomtazi,et al.  Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS , 2013 .

[19]  A. H. Nilson,et al.  Mechanical Properties of High-Strength Lightweight Concrete , 1986 .

[20]  Tao Yu,et al.  Finite element modeling of confined concrete-I: Drucker–Prager type plasticity model , 2010 .

[21]  Wan-Yang Gao,et al.  Finite element modeling of reinforced concrete beams exposed to fire , 2013 .

[22]  Yang Ke.  Caractérisation du comportement mécanique des bétons de granulats légers : expérience et modélisation , 2008 .

[23]  K. Kovler,et al.  Prevention of autogenous shrinkage in high-strength concrete by internal curing using wet lightweight aggregates , 2001 .

[24]  Ehab Ellobody,et al.  Modelling of unbonded post-tensioned concrete slabs under fire conditions , 2009 .

[25]  H. Dumontet,et al.  Influence of volume fraction and characteristics of lightweight aggregates on the mechanical properties of concrete , 2009 .

[26]  Jui-Sheng Chou,et al.  Concrete compressive strength analysis using a combined classification and regression technique , 2012 .

[27]  M. Polak,et al.  Finite element analysis of punching shear of concrete slabs using damaged plasticity model in ABAQUS , 2015 .

[28]  Seongkyu Chang,et al.  Application of Probabilistic Neural Networks for Prediction of Concrete Strength , 2005 .

[29]  Ran Huang,et al.  Approximate Strength of Lightweight Aggregate Using Micromechanics Method , 1998 .

[30]  Alaa G. Sherif,et al.  Finite element analysis of reinforced concrete beams with opening strengthened using FRP , 2017 .

[31]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[32]  Tadayoshi Fushiki,et al.  Estimation of prediction error by using K-fold cross-validation , 2011, Stat. Comput..

[33]  Surendra M. Gupta,et al.  Support Vector Machines based Modelling of Concrete Strength , 2008 .

[34]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.