Tavana Amlashi, Amir, Ghanizadeh, Ali Reza, Abbaslou, Hakime, Alidoust, Pourya. (1398). Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries. نشریه مهندسی عمران امیرکبیر, 4(1), 37-54. doi: 10.22060/ajce.2019.15026.5517
Amir Tavana Amlashi; Ali Reza Ghanizadeh; Hakime Abbaslou; Pourya Alidoust. "Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries". نشریه مهندسی عمران امیرکبیر, 4, 1, 1398, 37-54. doi: 10.22060/ajce.2019.15026.5517
Tavana Amlashi, Amir, Ghanizadeh, Ali Reza, Abbaslou, Hakime, Alidoust, Pourya. (1398). 'Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries', نشریه مهندسی عمران امیرکبیر, 4(1), pp. 37-54. doi: 10.22060/ajce.2019.15026.5517
Tavana Amlashi, Amir, Ghanizadeh, Ali Reza, Abbaslou, Hakime, Alidoust, Pourya. Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries. نشریه مهندسی عمران امیرکبیر, 1398; 4(1): 37-54. doi: 10.22060/ajce.2019.15026.5517

Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries

AUT Journal of Civil Engineering
مقاله 4، دوره 4، شماره 1، بهار 2020، صفحه 37-54    اصل مقاله (1.44 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/ajce.2019.15026.5517
نویسندگان
Amir Tavana Amlashi* 1؛ Ali Reza Ghanizadeh2؛ Hakime Abbaslou2؛ Pourya Alidoust3
1Young Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran
2Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran
3PhD candidate of Geotechnical & Geo-Environmental Engineering, Southern Illinois University Carbondale
چکیده
Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This type of concrete shows great promise    to satisfy the requirements of the strength, stiffness and permeability for remedial cut-off wall construction. This paper aims to explore three hybrid machine learning algorithms including Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized with Particle Swarm Optimization (PSO) to predict the compressive and splitting tensile strength of plastic concretes. To this end, data were collected from different sources and data gaps were covered by extra experimental tests and finally, 387 data for compressive strength and 107 data for splitting tensile strength were gathered for modeling. This study shows that ANN-PSO is superior to SVM-PSO and ANFIS-PSO in case of predicting compressive as well as splitting tensile strength   of plastic concretes. The coefficient of determination (R2) in case of ANN-PSO for both training and testing sets is more than 0.95. Results of this study can be used to predict the compressive and splitting tensile strength of plastic concretes with regards to constituent materials and specimen geometry of plastic concrete.
کلیدواژه‌ها
plastic concrete؛ compressive strength؛ splitting tensile strength؛ machine learning algorithms؛ particle swarm optimization
مراجع
آمار
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