Modeling Deep Beam Strengths with a Genetic Programming System

This paper proposes a genetic programming system (GPS) to both predict and program deep beam strength. The soft prediction is to provide estimated results for problems and the soft programming is to represent problems with meaningful formulas. In proposed GPS, two models of genetic programming (GP) and weighted genetic programming (WGP) are involved. GP is structured of a tree topology, inputs, and operators. WGP improves GP on introducing weights to balance tree blanches. Both GP and WGP results are provided in the GPS. Besides, this paper further designed a WGP to provide polynomial-like formulas, which seemed pretty helpful to parameter studies. Deep beam strengths were used as the case study. Results reveal that GPS has two distinct advantages over other black-box approaches in programming formulas and parameter studies. Furthermore, GPS does not surrender in prediction accuracy at all. Summarily, uses of proposed GPS are to predict, program, and model engineering problems.

[1]  Ardeshir Bahreininejad,et al.  Damage detection of truss bridge joints using Artificial Neural Networks , 2008, Expert Syst. Appl..

[2]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[3]  Ajith Abraham,et al.  A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria , 2001, HIS.

[4]  Hsing-Chih Tsai.  Weighted operation structures to program strengths of concrete-typed specimens using genetic algorithm , 2011, Expert Syst. Appl..

[5]  Hsing-Chih Tsai,et al.  Using weighted genetic programming to program squat wall strengths and tune associated formulas , 2011, Eng. Appl. Artif. Intell..

[6]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[7]  Frank Kerekes,et al.  Fifty Years of Development in Building Code Requirements for Reinforced Concrete , 1954 .

[8]  Lale Özbakir,et al.  Prediction of compressive and tensile strength of limestone via genetic programming , 2008, Expert Syst. Appl..

[9]  I-Cheng Yeh,et al.  Knowledge discovery of concrete material using Genetic Operation Trees , 2009, Expert Syst. Appl..

[10]  Hsing-Chih Tsai,et al.  Hybrid high order neural networks , 2009, Appl. Soft Comput..

[11]  Maziar Palhang,et al.  Generalization performance of support vector machines and neural networks in runoff modeling , 2009, Expert Syst. Appl..

[12]  Hsing-Chih Tsai,et al.  Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization , 2010, Expert Syst. Appl..

[13]  Adil Baykasoglu,et al.  Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches , 2009, Expert Syst. Appl..

[14]  M. Oltean,et al.  Multi Expression Programming , 2021 .