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Abstract
Strength and deformation characteristics of a rock play a remarkable role in designing any geotechnical structure connected to rock mass. This study aims to propose a practical intelligence system, namely the group method of data handling (GMDH) for indirect rock deformation prediction. Direct measurement of rock deformation in laboratory is time consuming, difficult and costly. In the current study, several rock index tests were conducted, together with unconfined compressive strength tests, on collected granitic block samples. In this study, in accordance to the first set objective, four empirical equations were proposed based on predictors, including Schmidt hammer rebound number, p-wave velocity, porosity and point load strength index, aiming to predict rock deformation. The results of these analyses confirmed that there is a need to develop new multiple-input models in predicting rock deformation. To this end, a GMDH model was designed to forecast rock deformation. Aiming to obtain a fair comparison, a pre-developed artificial neural network (ANN), as a benchmark model of intelligence systems, was also developed to predict rock deformation. Then, through the use of some well-known performance indices, the GMDH and pre-developed ANN models were assessed and their results were compared to select the best predictive model amongst them. Results confirmed that the GMDH is a powerful and robust technique to the reliable prediction of rock deformation.
Key Words
rock deformation; indirect measure; predictive model; group method of data handling; artificial neural network
Address
Danial Jahed Armaghani:Department of Civil Engineering, Faculty of Engineering, University of Malaya,
50603, Lembah Pantai, Kuala Lumpur, Malaysia
Ehsan Momeni: Faculty of Engineering, Lorestan University, Khorramabad, Iran
Panagiotis G. Asteris:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,
14121 Heraklion, Athens, Greece
Abstract
Real-world engineering problems deal with nonlinear, nonconvex, and discontinuous solution space. Due to the high level of complexity, state-of-art methods are required for handling this sort of NP-hard type problem. One of the most efficient strategies is considering metaheuristic optimization algorithms to facilitate them. Civil engineering problems because of the high level of uncertainties and effective parameters have been the subject of many optimization-based studies. In this paper, the main effort was to provide an overview of different applications of optimization algorithms for civil engineering problems. Moreover, we classified a large number of available studies on the implementation of metaheuristics in various fields of civil engineering classified in this study and highlighted the most important features of them to provide an efficient reference for active researchers in this field.
Key Words
engineering optimization; civil engineering; global optimization; metaheuristic algorithms
Address
Ali R. Kashani,Charles V. Camp,Mehdi Rostamian :1Department of Civil Engineering, University of Memphis, Memphis, TN 38152, U.S.A.
Mostafa Gandomi:School of Civil Engineering, University of Tehran, Tehran, Iran
H. Gandomi:Faculty of Engineering & IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
Abstract
Reliability analysis of the geo-structures has contributed a lot to the field of Geotechnical Engineering. This area of study gives an overview of the probability of failure of different structures. First-order second-moment method (FOSM) is a method, incorporated in this study, to determine the reliability index of the geo-structures (and other structures as well). In this paper, design of retaining wall is modelled using Functional Network (FN), Genetic Programming (GP) and Group Method of Data Handling (GMDH). These soft computing techniques have removed the cumbersome nature of the problem and have increased the precision of the result. The uncertainties involved in this problem is reduced. As these methodologies are evolved and are heated topics in the artificial intelligence field, they have eliminated the drawbacks of several other soft computing methods involved previously in the reliability problems. These methodologies employ genetic algorithm (GMDH) and make use of domain knowledge along with data knowledge accordingly (FN). These techniques have made problems facile and can produce a precise result. Performance of these methods has been assessed using different performance analysis, criterions and parameters. This paper is a comparative study between FOSM, FN based FOSM, GP based FOSM and GMDH based FOSM.
Key Words
Address
Pratishtha Mishra, Pijush Samui and Sanjeev Sinha:Department of Civil Engineering Department, National Institute of Technology Patna, Bihar, India
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On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength
P.G. Asteris, M. Apostolopoulou, D.J. Armaghani, L. Cavaleri, A.T. Chountalas, D. Guney, M. Hajihassani, M. Hasanipanah, M. Khandelwal, C. Karamani, M. Koopialipoor, E. Kotsonis, T.-T. Le, P.B. Lourenço, H.-B. Ly, A.Moropoulou, H. Nguyen, B.T. P |
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pages 063-99. |
DOI: 10.12989/mca.2020.1.1.063 |
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Abstract
In recent years, metakaolin, as a highly reactive pozzolan, has been in the center of research concerning mortar-based materials. Metakaolin is used as an addition in cement-mortars, substituting the cement fraction to a certain extent, in order to enhance sustainability of cement mortars, both in terms of environmental impact of raw materials production, as well as in terms of improving cement-based mortars durability under environmental actions. However, as metakaolin affects the mechanical performance of cement-based mortars, it is important to know the compressive strength that these blended mortars achieve at 28-days, in terms of structural design. Toward this direction, metaheuristic models such as ANN and Genetic Programming (GP) models have been developed and trained through the use of a database, compiled by available, in the literature, experimental works related to cement and blended cement-metakaolin mortars. In the model development phase, the most important parameters affecting the strength of concrete-based mortars, were investigated and selected. In addition, the effect of the selected transfer functions, as well as the initial values of weights and biases on the performance of ANN models, were also investigated. Based on this analysis, it was shown that ANNs with selected transfer functions (such as the RadialBasis transfer function, the Soft-Max transfer function, and the Normalized Radial Basis transfer function) were, able to reliably simulate the 28-days compressive strength of the cement-based mortars. In addition, it was shown that parameters such as the cement grade and the maximum diameter of aggregates, are very important in determining compressive strength of the cement-based mortars; this is an important finding, because these parameters are usually not taken into account in the research studies concerned in the prediction of compressive strength through computational models.
Key Words
artificial neural networks (ANNs); cement; compressive strength; Genetic Programming (GP); metakaolin; mortar; metaheuristic algorithms; surrogate models
Address
Panagiotis G. Asteris:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,
Heraklion, GR 14121, Athens, Greece
Maria Apostolopoulou:School of Chemical Engineering, National Technical University of Athens, Zografou Campus,
15780, Athens, Greece
Danial J. Armaghani:Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
Liborio Cavaleri:Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM),
University of Palermo, Palermo, Italy
Athanasios T. Chountalas:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,Heraklion, GR 14121, Athens, Greece
Deniz Guney:1omputational Mechanics Laboratory, School of Pedagogical and Technological Education,Heraklion, GR 14121, Athens, Greece
Mohsen Hajihassani:Construction Research Alliance, Universiti Teknologi Malaysia, UTM Skudai, 81310 Johor, Malaysia
Mahdi Hasanipanah:Department of Mining Engineering, University of Kashan, Kashan, Iran
Manoj Khandelwal:Federation University Australia, PO Box 663, Ballarat, VIC 3353, Australia
Chrysoula Karamani:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,
Heraklion, GR 14121, Athens, Greece
Mohammadreza Koopialipoor:Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran
Evgenios Kotsonis:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,
Heraklion, GR 14121, Athens, Greece
Tien-Thinh Le:Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Paulo B. Lourenço:ISISE, Department of Civil Engineering, University of Minho, Azurém, 4800-058 Guimarães, Portugal
Hai-Bang Ly:Computational Mechanics Laboratory, School of Pedagogical and Technological Education,Heraklion, GR 14121, Athens, Greece
Antonia Moropoulou:School of Chemical Engineering, National Technical University of Athens, Zografou Campus,15780, Athens, Greece
Hoang Nguyen:Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam
Binh Thai Pham:University of Transport Technology, Hanoi 100000, Vietnam
Pijush Samui:Department of Civil Engineering, NIT Patna, Patna – 800005, Bihar, India
Jian Zhou:School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Abstract
This paper seeks to estimate and predict the global price of silver as a strategic metal using a combined multiple linear regression (MLR) and imperialist competitive algorithm (ICA). For this purpose, the global silver, copper, and aluminum prices were studied during 2009-2019. Then, the global prices of silver, copper, and aluminum were considered each as one of the input parameters, and, in return, the silver price was chosen as the target parameter. Using the Table Curve 2D & 3D software, the comprehensive statistical relationships between the input and output parameters were specified and suggested. Subsequently, the SPSS v25 software and the stepwise method were used to suggest the best nonlinear regression relationship with the 85% confidence level. Eventually, the optimal coefficients of the proposed statistical relation were determined by applying the ICA, which resulted in the improving results and also the reducing prediction error up to 1%.
Key Words
metal price; forecasting; statistical analyses; imperialist competitive algorithm
Address
Behshad Jodeiri Shokri, Hesam Dehghani and Reza Shamsi:Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
