Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
Neural Network based model to Estimate Dynamic Modulus E* for mixtures in Costa Rica
1
15
EN
Fabricio
Leiva-Villacorta
0000-0003-2506-9752
Auburn University
leivafa@auburn.edu
Adriana
Vargas-Nordcbeck
Auburn University
vargaad@auburn.edu
10.22115/scce.2019.188006.1110
Various dynamic modulus (E*) predictive models have been developed to estimate E* as an alternative to laboratory testing. The most widely used model is the 1999 I-37A Witczak predictive equation based on North American mixtures laboratory results. The differences in material properties, traffic information, and environmental conditions for Latin American countries make it necessary to calibrate these models using local conditions. Consequently, the National Laboratory of Materials and Structural Models at the University of Costa Rica (in Spanish, LanammeUCR) has previously performed a local calibration of this model based on E* values for different types of Costa Rican mixtures. However, further research has shown that there is still room for improvement in the accuracy of the calibrated model (Witczak-Lanamme model) based on advanced regression techniques such as neural networks (NN). <br />The objective of this study was to develop an improved and more effective dynamic modulus E* predictive regression model for mixtures in Costa Rica by means of NN based models. A comparison of the predicted E* values among the Witczak model, Witczak-Lanamme model and the new and improved model based on artificial neural networks (E* NN- model) indicated that the former not only met the model adequacy checking criteria but also exhibited the best goodness of fit parameters and the lowest overall bias. The findings of this study also supported the use of more advanced regression techniques that can become a more attractive alternative to local calibration of the Witczak I-37A equation.
Neural Network,dynamic modulus,Asphalt mixtures,Pavements,Master Curves
http://www.jsoftcivil.com/article_89875.html
http://www.jsoftcivil.com/article_89875_f36847fe5b0c58e891af2103be4ef840.pdf
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
Evolutionary algorithm performance evaluation in project time-cost optimization
16
29
EN
Athanasios
Chassiakos
0000-0001-9175-4390
Department of Civil Engineering, University of Patras
a.chassiakos@upatras.gr
George
Rempis
Department of Civil Engineering, University of Patras
georgerempis@gmail.com
10.22115/scce.2019.155434.1091
The time-cost trade-off problem pertains to the assessment of the best method of activity construction so that a project is completed within a given deadline and at least cost. Although several evolutionary-type of algorithms have been reported over the last two decades to solve this NP-hard combinatorial problem, there are not many comparative studies independently evaluating several methods. Such studies can provide support to project managers regarding the selection of the appropriate method. The objective of this work is to comparatively evaluate the performance potential of a number of evolutionary algorithms, each one with its own variations, for the time-cost trade-off problem. The evaluation is based on two measures of effectiveness, the solution quality (accuracy) and the processing time to obtain the solution. The solution is sought via a general purpose commercial optimization software without much interference in algorithm parameter setting and fine-tuning in an attempt to follow the anticipated project manager approach. The investigation has been based on case studies from the literature with varying project size and characteristics. Results indicate that certain structures of genetic algorithms, particle swarm optimization, and differential evolution method present the best performance.
Evolutionary Algorithm,optimization,Project scheduling,Time-Cost Trade-Off,Construction management
http://www.jsoftcivil.com/article_89544.html
http://www.jsoftcivil.com/article_89544_8cb3f834518e8f2c0348474a18c468b0.pdf
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
Application of Soft Computing Techniques in Predicting the Ultimate Bearing Capacity of Strip Footing Subjected to Eccentric Inclined Load and Resting on Sand
30
43
EN
Rakesh
Dutta
0000-0002-4611-9950
Department of Civil Engineering, NIT Hamirpur, Himachal Pradesh, India
rakeshkdutta@yahoo.com
Tammineni
Gnanananda
Rao
0000-0002-3332-8083
Ph.D. Scholar
anandrcwing@gmail.com
Vishwas
Nandakishor
Khatri
Civil Engineering Department, Indian Institute of Technology (Indian School of Mines), Dhanbad
vishuiisc@gmail.com
10.22115/scce.2019.144535.1088
The present study attempts to predict the ultimate bearing capacity (UBC) of the strip footing resting on sand and subjected to inclined load having eccentricity with respect to the vertical using three different soft computing techniques such as support vector mechanism with radial basis function (SVM RBF kernel), M5P model tree (M5P) and random forest regression (RFR). The UBC was computed in the form of reduction factor and this reduction factor was assumed to be dependent on the ultimate bearing capacity (qu) of the strip footing subjected to vertical load, eccentricity ratio (e/B), inclination ratio (α/ϕ) and the embedment ratio (Df/B). The performance of each model was analyzed by comparing the statistical performance measure parameters. The outcome of present study suggests that SVM RBF kernel predicts the reduction factor with least error followed by M5P and RFR. All the model predictions further outperformed those based on semi-empirical approach available in literature. Finally, sensitivity analysis performed for the SVM RBF kernel model which suggests that the inclination ratio (α/ϕ) and eccentricity ratio (e/B) was an important parameter, in comparison to other parameters, considered for predicting the reduction factor.
Ultimate bearing capacity eccentrically inclined load,Reduction factor,SVM RBF kernel,M5P model tree,Random forest regression
http://www.jsoftcivil.com/article_89749.html
http://www.jsoftcivil.com/article_89749_d41d8cd98f00b204e9800998ecf8427e.pdf
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
MODELLING OF CONCRETE COMPRESSIVE STRENGTH ADMIXED WITH GGBFS USING GENE EXPRESSION PROGRAMMING
44
58
EN
Abejide
O
Samuel
Civil engineering Department, faculty of engineering, Ahmadu Bello University
abejideos@yahoo.com
10.22115/scce.2019.178214.1103
Several studies have established that strength development in concrete is not only determined by the water/binder ratio, but it is also affected by the presence of other ingredients. With the increase in the number of concrete ingredients from the conventional four materials by addition of various types of admixtures (agricultural wastes, chemical, mineral and biological) to achieve a desired property, modelling its behavior has become more complex and challenging. Presented in this work is the possibility of adopting the Gene Expression Programming (GEP) algorithm to predict the compressive strength of concrete admixed with Ground Granulated Blast Furnace Slag (GGBFS) as Supplementary Cementitious Materials (SCMs). A set of data with satisfactory experimental results were obtained from literatures for the study. Result from the GEP algorithm was compared with that from stepwise regression analysis in order to appreciate the accuracy of GEP algorithm as compared to other data analysis program. With R-Square value and MSE of -0.94 and 5.15 respectively, The GEP algorithm proves to be more accurate in the modelling of concrete compressive strength.
Concrete,Strength,GGBFS,Gene expression programming
http://www.jsoftcivil.com/article_91665.html
http://www.jsoftcivil.com/article_91665_d41d8cd98f00b204e9800998ecf8427e.pdf
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
Machine Learning Method for predicting the depth of shallow lakes Using Multi-Band Remote Sensing Images
59
68
EN
Amin
Jalilzadeh
Department of Civil Engineering, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
amin.jalilzade12@gmail.com
Saeed
Behzadi
Department of Civil Engineering, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
behzadi.saeed@gmail.com
10.22115/scce.2019.196533.1119
Knowing the lake’s characteristics information such as depth is an essential requirement for the water managers; however, conducting a comprehensive bathymetric survey is considered as a difficult task. After the advent of remote sensing, and satellite imagery, it has been recognized that water depth can be estimated in some way over shallow water. There are many models that can evaluate relationships between multi-band images, and depth measurements; however, artificial computation methods can be used as an approximation tool for this issue. They are also considered as fairly simple and practical models to estimate depth in shallow waters. In this paper, different methods of artificial computation are used to calculate the depth of shallow lake, then these methods are compared. The results show that Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and regression learner are best methods for this issue with RMSE 0.8 , 1.47, and 0.96 respectively.
Remote Sensing,geographic information systems (GIS),artificial computation,Bathymetry
http://www.jsoftcivil.com/article_95794.html
http://www.jsoftcivil.com/article_95794_d41d8cd98f00b204e9800998ecf8427e.pdf
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
Prediction of Flexural strength of Concrete Produced by Using Pozzolanic Materials and Partly Replacing NFA by MS
69
77
EN
kiran
Mansingrao
Mane
0000-0003-1185-9059
Civil Engineering Department
Research Scholar
SDMCET Dharward, Karnataka, India
kiranmane818@gmail.com
D
K
Kulkarni
Civil Engineering , Professor , SDMCET Dharwad
dilipkkulkarni@rediffmail.com
K
B
Prakash
Civil ,Engineering , Principal , GEC Haveri
kbprakash04@rediffmail.com
10.22115/scce.2019.197000.1121
The use of huge quantity of natural fine aggregate (NFA) and cement in civil construction work which have given rise to various ecological problems. The industrial waste like Blast furnace slag (GGBFS), fly ash, metakaolin, silica fume can be used as partly replacement for cement and manufactured sand obtained from crusher, was partly used as fine aggregate .In this work, MATLAB software model is developed using neural network toolbox to predict the flexural strength of concrete made by using pozzolanic materials and partly replacing natural fine aggregate (NFA) by Manufactured sand(MS). Flexural strength was experimentally calculated by casting beams specimens and results obtained from experiment were used to develop the artificial neural network (ANN) model. Total 131 results values were used to modeling formation and from that 30% data record was used for testing purpose and 70% data record was used for training purpose. 25 input materials properties were used to find the 28 days flexural strength of concrete obtained from partly replacing cement with pozzolans and partly replacing natural fine aggregate (NFA) by manufactured sand (MS). The results obtained from ANN model provides very strong accuracy to predict flexural strength of concrete obtained from partly replacing cement with pozzolans and natural fine aggregate (NFA) by manufactured sand.
pozzolanic materials,Manufactured sand,Flexural Strength,Artificial Neural Network
http://www.jsoftcivil.com/article_95795.html
http://www.jsoftcivil.com/article_95795_d41d8cd98f00b204e9800998ecf8427e.pdf
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
3
2
2019
04
01
Comparison of Genetic Algorithm(GA) and Particle Swarm Optimization Algorithm(PSO) for Discrete and Continuous Size Optimization of 2D Truss Structures
78
98
EN
Mahmood
Akbari
0000-0002-1319-6079
Assistant Professor - Civil Engineering Department, University of Kashan, Kashan, Iran
makbari@kashanu.ac.ir
Mojtaba
Henteh
PhD Candidate- Structure Engineering, Civil Engineering Department, University of Semnan, Semnan, Iran
mhenteh@semnan.ac.ir
10.22115/scce.2019.195713.1117
Optimization of truss structures including topology, shape and size optimization were investigated by different researchers in the previous years. The aim of this study is discrete and continuous size optimization of two-dimensional truss structures with the fixed topology and the shape. For this purpose, the section area of the members are considered as the decision variables and the weight minimization as the objective function. The constraints are the member stresses and the node displacements which should be limited at the allowable ranges for each case.<br />In this study, Genetic Algorithm and Particle Swarm Optimization algorithm are used for truss optimization. To analyse and determine the stresses and displacements, OpenSees software is used and linked with the codes of Genetic Algorithm and Particle Swarm Optimization algorithm provided in the MATLAB software environment.<br />In this study, the optimization of four two-dimensional trusses including the Six-node, 10-member truss, the Eight-node, 15-member truss, the Nine-node, 17-member truss and the Twenty-node, 45-member truss under different loadings derived from the literature are done by the Genetic Algorithm and Particle Swarm Optimization algorithm and the results are compared with those of the other researchers. The comparisons show the outputs of the Genetic Algorithm are the most generally economical among the different studies for the discrete size cases while for the continuous size cases, the outputs of the Particle Swarm Optimization algorithm are the most economical.
Particle Swarm Optimization Algorithm,Genetic Algorithm,2D Truss Structures,discrete and continuous sizes,OpenSees
http://www.jsoftcivil.com/article_95950.html
http://www.jsoftcivil.com/article_95950_d41d8cd98f00b204e9800998ecf8427e.pdf