eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
1
23
10.22115/scce.2020.236807.1239
113898
Hydrologic Performance of Drainage Network Under Different Climatic and Land-Use Conditions, A Case Study of Chattogram
Mehedi Hassan Masum
mehedi.ce.cuet@gmail.com
1
Jobayath Hossesn
jobayathfd@gmail.com
2
Sudip Pal
sudip.ce.cuet@gmail.com
3
PG Student, Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattoram, Bangladesh
Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattoram, Bangladesh
Professor, Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattoram, Bangladesh
The recent alternation of urban hydrology is seen significant due to the growth of urban sprawl. In the changed urban hydrology and urban settings, the city drainage is seen underperformed and problems are manifolds. This study therefore aims to evaluate the hydrologic performance of drainage under different land use patterns demonstrating urbanization effects using the Mahesh Khal in Chattogram as a studied watershed. This study analyses land use pattern of the study area with the data collected through field investigation and also gathered from the secondary sources using ArcGIS 10.4. The change patterns are realized portraying scenarios with statistical significance. The study revealed that the trends of built-up area is significantly high figuring out over doubled in last 30 years period; 24% in 1998 to 53% in 2018 compromising the lost of open water and vegetative surfaces. In align with such changes, the peak discharges found for 2, 5, 10, 25, 50 and 100 years return period were 20, 29, 36, 44, 50 and 57 , respectively, and were seen varied mostly with the curve number and imperviousness. The discharge in combination with tidal inflow into the Khal exceed the capacity of the existing capacity and is seen underperformed. The dumping of solid wastes, improper management of Khal, changes in surface slopes of connecting drains are found key factors. The study suggests that maintaining vegetation and surface slopes may increase the performances of drainage.
https://www.jsoftcivil.com/article_113898_ca72d8238322077acc5863f0c0273e5e.pdf
ArcGIS
HEC-HMS
CN
runoff
Manning’s n
eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
24
35
10.22115/scce.2020.228611.1213
113899
Compressive Strength Estimation of Mesh Embedded Masonry Prism Using Empirical and Neural Network Models
S. Kanchidurai
kanchidurai@civil.sastra.edu
1
P.A. Krishnan
krishananpanitt@gmail.com
2
K. Baskar
baskernitt44@gmail.com
3
Assistant Professor, School of Civil Engineering, SASTRA Deemed to be University, Thanjavur, India
Professor, Department of Civil Engineering, National Institute of Technology, Trichy, India
Professor, Department of Civil Engineering, National Institute of Technology, Trichy, India
Presently, the mesh embedment in masonry is becoming a trendy research topic. In this paper, the mesh embedded masonry prism was cast and tested. The experimental data were used for the analytical modelling. Compressive strength (CS) test was conducted for forty five masonry prism specimens with and without poultry netting mesh (PNM) embedment in the bed joints. The small mesh embedment in the masonry prism provides the better strength improvement as well as the endurance. The size of masonry prism was 225×105×176 mm. Uniformity was maintained in all prisms as per the guidelines given in ASTM C1314. Compressive strength experimental results are compared with a new proposed regression equation. The equation needs nine input parameters and two adjustment coefficients. The masonry mortar strength and mesh embedment are considered as input parameter. The experimental results were predicted by proposed Artificial Neural Network model. The validated results were gives better and more accuracy compared to the statistical and MLRPM models.
https://www.jsoftcivil.com/article_113899_127f4fb40fcac9f83540a7dda95421c1.pdf
Masonry
PNM mesh
Compressive strength
analytical modelling
statistical
Artificial neural network (ANN)
Multiple linear regression predictive model (MLRPM)
eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
36
46
10.22115/scce.2020.242813.1251
114273
Simulation of Monthly Precipitation in Semnan City Using ANN Artificial Intelligence Model
Hamidreza Ghazvinian
hamidrezaghazvinian@semnan.ac.ir
1
Hossein Bahrami
hosseinbahrami90hb@gmail.com
2
Hossein Ghazvinian
hgh1997g@gmail.com
3
Salim Heddam
heddamsalim@yahoo.fr
4
Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Faculty of Civil Engineering, Semnan University, Semnan, Iran
Faculty of Architecture and Urban Engineering, Semnan University, Semnan, Iran
Professor, Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Skikda, Algeria
Precipitation forecasting is of great importance in various aspects of catchment management, drought, and flood warning. Precipitation is regarded as one of the important components of the water cycle and plays a crucial role in measuring the climatic characteristics of each region. The present study aims to forecast monthly precipitation in Semnan city by using artificial neural networks (ANN). For this purpose, we used the minimum and maximum temperature data, mean relative humidity, wind speed, sunshine hours, and monthly precipitation during a statistical period of 18 years (2000-2018). Moreover, an artificial neural network was used as a nonlinear method to simulate precipitation. In this research, all data were normalized due to the different units of inputs and outputs in the forecasting model. Further, seven different scenarios were considered as input for the ANN model. Totally, 70% of the data were used for training while the other 30% were used for testing. The model was evaluated with appropriate statistics such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Scenario 6, which included the inputs of minimum and maximum temperature, mean relative humidity, wind speed, and pressure, provided the best performance compared to other scenarios. The values of , RMSE, and MAE for the superior scenario were 0.8597, 4.0257, and 2.3261, respectively.
https://www.jsoftcivil.com/article_114273_3cd3279a8aeb7f3028f522c451173d00.pdf
Monthly precipitation
Precipitation forecasting
Artificial Neural Network
Semnan
eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
47
60
10.22115/scce.2020.232778.1223
114716
Assessment of the Modulus of Elasticity at a Triaxial Stress State for Rocks Using Gene Expression Programming
Umit Atici
uatici@ohu.edu.tr
1
Professor, Department of Mining Engineering, Nigde Omer Halisdemir University, Nigde, Turkey
Rocks were subjected to the deformation test under five different confining stresses (0, 5, 10, 15, and 20 MPa) using the Hoek cell to determine changes in the elastic properties of the rocks under confining stress, and the results were evaluated based on density, porosity, Schmidt hardness, and compressive strength. A total of nine different rocks, two granites, two andesites, two limestones, one tuff, one diorite, and one marble, were used. When the confining stress was increased from 0 MPa to 5 MPa and from 5 MPa to 10 MPa, elasticity increased by approximately 20%. When the confining stress was increased from 10 MPa to 20 MPa, the increase was 7% in comparison with the previous value. Then, to formulate the modulus of elasticity for rocks under the triaxial stress conditions, a new and intelligent approach to gene application, gene expression programming was utilized. The success of the model was thoroughly assessed based on measurable criteria such as the root mean square error, mean absolute percentage error, and coefficient of determination. Furthermore, the success of the model was comprehensively assessed based on the model testing, and 0.88 and 0.81 R2 values were obtained for training and validation, respectively. The performance of the gene expression programming-based formulation was compared with the formulae previously proposed in the literature. The gene expression method exhibited the best performance, and it was identified to calculate the modulus of elasticity under triaxial stress conditions more effectively.Then, to formulate the modulus of elasticity for rocks under the triaxial stress conditions, a new and intelligent approach to gene application, gene expression programming was utilized. The success of the model was thoroughly assessed based on measurable criteria such as the root mean square error, mean absolute percentage error, and coefficient of determination. Furthermore, the success of the model was comprehensively assessed based on the model testing, and 0.88 and 0.81 R2 values were obtained for training and validation, respectively. The performance of the gene expression programming-based formulation was compared with the formulae previously proposed in the literature. The gene expression method exhibited the best performance, and it was identified to calculate the modulus of elasticity under triaxial stress conditions more effectively.
https://www.jsoftcivil.com/article_114716_b249bef510b86bec0a8ada6f2422ad0c.pdf
Triaxial stress state
confining stress
modulus of elasticity
Modeling
Gene expression programming
eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
61
78
10.22115/scce.2020.213196.1153
115527
Modeling of Confined Circular Concrete Columns Wrapped by Fiber Reinforced Polymer Using Artificial Neural Network
Mahdi Abbaszadeh
dr.mahdi.abbaszadeh@gmail.com
1
Mohammad Kazem Sharbatdar
msharbatdar@semnan.ac.ir
2
Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Iran
Associate Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
This study is aimed to explore using an artificial neural network method to anticipate the confined compressive strength and its corresponding strain for the circular concrete columns wrapped with FRP sheets. 58 experimental data of circular concrete columns tested under concentric loading were collected from the literature. The experimental data is used to train and test the neural network. A comparative study was also carried out between the neural network model and the other existing models. It was found that the fundamental behavior of confined concrete columns can logically be captured by the neural network model. Besides, the neural network approach provided better results than the analytical and experimental models. The neural network-based model with R2 equal to 0.993 and 0.991 for training and testing the compressive strength, respectively, shows that the presented model is a practical method to predict the confinement behavior of concrete columns wrapped with FRP since it provides instantaneous result once it is appropriately trained and tested.
https://www.jsoftcivil.com/article_115527_fba3e1efd17167af0a3f779755abefdb.pdf
Concrete columns
CFRP
Confinement
Artificial Neural Networks
Models
eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
79
97
10.22115/scce.2020.246071.1254
117865
Modeling the Influence of Environmental Factors on Concrete Evaporation Rate
Vasileios Papadimitropoulos
infovpap@yahoo.com
1
Panagiotis Tsikas
ts_panos@yahoo.gr
2
Athanasios Chassiakos
a.chassiakos@upatras.gr
3
Ph.D. Candidate, Department of Civil Engineering, University of Patras, Patras, Greece
Ph.D., Department of Civil Engineering, University of Patras, Patras, Greece
Associate Professor, Department of Civil Engineering, University of Patras, Patras, Greece
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
https://www.jsoftcivil.com/article_117865_1e46197f271838ac0d2ed40016c0315b.pdf
Concrete evaporation rate
Plastic shrinkage
Hot weather concreting
Artificial Neural Networks
Genetic Algorithms
Curve-fitting
eng
Pouyan Press
Journal of Soft Computing in Civil Engineering
2588-2872
2020-10-01
4
4
98
111
10.22115/scce.2020.244833.1252
115587
Dual Target Optimization of Two-Dimensional Truss Using Cost Efficiency and Structural Reliability Sufficiency
Mohammad Rezaeemanesh
m_rezaeemanesh@yahoo.com
1
Seyed Hooman Ghasemi
seyedhooman.ghasemi@wsu.edu
2
Mansoureh Rezaeemanesh
mrezaeemanesh@yahoo.com
3
M.Sc. Graduated, Department of Mechanical Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran
Department of Civil and Environmental Engineering, Washington State University, Pullman, United States
M.Sc. Graduated, Faculty of Civil, Water and Environment Engineering, Shahid Beheshti University, Tehran, Iran
The main contribution of this study is to open a discussion regarding the structural optimization associated with the cost efficiency and structural reliability sufficiency consideration. To do so, several various optimization approaches are investigated to deliberate both cost and reliability concerns. Particularly, particle swarm optimization is highlighted as a reliable optimization approach. Accordingly, an illustrative example is rendered to compare the feasibility of the considered optimization approaches. The feasibility of the investigated approaches is evaluated using the cost and reliability analysis. For the considered example, it was observed that the PSO optimization algorithm has multiple advantages such as easy realization, fast convergence, and promising performance in nonlinear performance optimization. The PSO optimization algorithm can be successfully applied in various fields of civil engineering. This popularity is due to the understandable performance of the PSO as well as its simplicity. In this paper, first, the literature on the subject has been described by two-dimensional truss analysis using the finite element method and optimized using the PSO particle swarm algorithm. A comparison of the results with this reference indicates the accuracy of this particle swarm algorithm in truss optimization. Indeed, this study ignites two main insights in structural optimizations assessment. The first illustration is related to how to establish a framework for structural system reliability analysis associated with the different degrees of indeterminacies. And the second illustration is related to making a decision problem concerning the structural optimization while both cost and reliability metric are two main parameters for the construction point of the view.
https://www.jsoftcivil.com/article_115587_da011f906cd241052c9e58050a944f44.pdf
Particle Swarm Optimization (PSO)
2-D truss
Finite Element
optimization