ORIGINAL_ARTICLE
Fuzzy-Based Approach to Predict the Performance of Shear Connectors in Composite Structures
Shear connectors in steel-concrete composite frames are essential elements to transfer the shear between steel and concrete. Several parameters must be considered in predicting the strength of these connectors. This research aims to estimate the performed rib shear strength of connectors in composite frames. To this end, four variables including the compressive strength of concrete, area of dowels, the transverse area in rib holes, and also connector height, are applied to a neuro-fuzzy model and the shear strength is selected as the target of the system. The model is trained using an experimental database and validated with an acceptable error. The estimated shear strength of connectors were satisfactorily similar to the measurements reported by the laboratories.
https://www.jsoftcivil.com/article_103850_a40350a12961c466ab610b51dff94556.pdf
2019-10-01
1
11
10.22115/scce.2020.215906.1165
Composite structures
Shear strength
Shear Connector
Concrete
Neuro-Fuzzy
Seyed Meisam
Kalantari
skalant4@uwo.ca
1
Department of Civil and Environmental Engineering, Western University, Ontario, Canada
AUTHOR
Seyedmehdi
Mortazavi
mortazavi635@gmail.com
2
Ph.D. Candidate, Faculty of Civil Engineering, Semnan University, Semnan, Iran
LEAD_AUTHOR
Mohammadsoroush
Tafazzoli
tafazzoli@wsu.edu
3
Assistant Professor, School of Design and Construction Management, Washington State University, Washington, United States
AUTHOR
[1] Allahyari H, M. Nikbin I, Rahimi R. S, Allahyari A. Experimental measurement of dynamic properties of composite slabs from frequency response. Measurement 2018;114:150–61. doi:10.1016/j.measurement.2017.09.030.
1
[2] Zheng S, Liu Y, Yoda T, Lin W. Parametric study on shear capacity of circular-hole and long-hole perfobond shear connector. J Constr Steel Res 2016;117:64–80. doi:10.1016/j.jcsr.2015.09.012.
2
[3] Ahn J-H, Kim S-H, Jeong Y-J. Shear behaviour of perfobond rib shear connector under static and cyclic loadings. Mag Concr Res 2008;60:347–57. doi:10.1680/macr.2007.00046.
3
[4] Naderpour H, Mirrashid M. Evaluation and Verification of Finite Element Analytical Models in Reinforced Concrete Members. Iran J Sci Technol Trans Civ Eng 2019. doi:10.1007/s40996-019-00240-8.
4
[5] Allahyari H, M. Nikbin I, Rahimi R. S, Heidarpour A. A new approach to determine strength of Perfobond rib shear connector in steel-concrete composite structures by employing neural network. Eng Struct 2018;157:235–49. doi:10.1016/j.engstruct.2017.12.007.
5
[6] Naderpour H, Mirrashid M. Classification of failure modes in ductile and non-ductile concrete joints. Eng Fail Anal 2019;103:361–75. doi:10.1016/j.engfailanal.2019.04.047.
6
[7] Mirrashid M. Comparison study of soft computing approaches for estimation of the non-ductile RC joint shear strength. Soft Comput Civ Eng 2017;1:12–28.
7
[8] Naderpour H, Mirrashid M. A computational model for Compressive Strength of Mortars Admixed with Mineral Materials,. Comput Eng Phys Model 2018;1:16–25.
8
[9] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.
9
[10] Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84. doi:10.1016/j.compstruct.2019.02.048.
10
[11] Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2019. doi:10.1016/j.jestch.2019.05.013.
11
[12] Jang J-SR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665–85. doi:10.1109/21.256541.
12
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13
[14] Naderpour H, Mirrashid M, Nagai K. An innovative approach for bond strength modeling in FRP strip-to-concrete joints using adaptive neuro–fuzzy inference system. Eng Comput 2019:1–18.
14
[15] Naderpour H, Mirrashid M. Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex Intell Syst 2019. doi:10.1007/s40747-019-00118-2.
15
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16
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17
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18
[19] Cândido-Martins JPS, Costa-Neves LF, Vellasco PCG da S. Experimental evaluation of the structural response of Perfobond shear connectors. Eng Struct 2010;32:1976–85. doi:10.1016/j.engstruct.2010.02.031.
19
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20
[21] Costa-Neves LF, Figueiredo JP, Vellasco PCG da S, Vianna J da C. Perforated shear connectors on composite girders under monotonic loading: An experimental approach. Eng Struct 2013;56:721–37. doi:10.1016/j.engstruct.2013.06.004.
21
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26
ORIGINAL_ARTICLE
Physical and Physic-Chemical Based Optimization Methods: A Review
Optimization techniques can be divided to two groups: Traditional or numerical methods and methods based on stochastic. The essential problem of the traditional methods, that by searching the ideal variables are found for the point that differential reaches zero, is staying in local optimum points, can not solving the non-linear non-convex problems with lots of constraints and variables, and needs other complex mathematical operations such as derivative. In order to satisfy the aforementioned problems, the scientists become interested on meta-heuristic optimization techniques, those are classified into two essential kinds, which are single and population-based solutions. The method does not require unique knowledge to the problem. By general knowledge the optimal solution can be achieved. The optimization methods based on population can be divided into 4 classes from inspiration point of view and physical based optimization methods is one of them. Physical based optimization algorithm: that the physical rules are used for updating the solutions are:, Lighting Attachment Procedure Optimization (LAPO), Gravitational Search Algorithm (GSA) Water Evaporation Optimization Algorithm, Multi-Verse Optimizer (MVO), Galaxy-based Search Algorithm (GbSA), Small-World Optimization Algorithm (SWOA), Black Hole (BH) algorithm, Ray Optimization (RO) algorithm, Artificial Chemical Reaction Optimization Algorithm (ACROA), Central Force Optimization (CFO) and Charged System Search (CSS) are some of physical methods. In this paper physical and physic-chemical phenomena based optimization methods are discuss and compare with other optimization methods. Some examples of these methods are shown and results compared with other well known methods. The physical phenomena based methods are shown reasonable results.
https://www.jsoftcivil.com/article_103456_47247acbfea8c212cf2f7bc55a94c1f1.pdf
2019-10-01
12
27
10.22115/scce.2020.214959.1161
Physical
Physic-Chemical
optimization
Optimization Technique
Behrooz
Vahidi
vahidi@aut.ac.ir
1
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Amin
Foroughi Nematolahi
amin.forooghi@aut.ac.ir
2
Ph.D. Student, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
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2
[3] Safaei A, Vahidi B, Askarian-Abyaneh H, Azad-Farsani E, Ahadi SM. A two step optimization algorithm for wind turbine generator placement considering maximum allowable capacity. Renew Energy 2016;92:75–82. doi:10.1016/j.renene.2016.01.093.
3
[4] Mirzaei M, Vahidi B. Feasibility analysis and optimal planning of renewable energy systems for industrial loads of a dairy factory in Tehran, Iran. J Renew Sustain Energy 2015;7:063114. doi:10.1063/1.4936591.
4
[5] Abootorabi Zarchi D, Vahidi B. Optimal placement of underground cables to maximise total ampacity considering cable lifetime. IET Gener Transm Distrib 2016;10:263–9. doi:10.1049/iet-gtd.2015.0949.
5
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6
[7] Kharazi S, Vahidi B, Hosseinian SH. Optimization Design of High Voltage Substation Ground Grid by Using PSO & HS Algorithms. Sci Int 2015;27:4011–8.
7
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8
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12
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13
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15
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16
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17
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20
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21
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22
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23
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24
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26
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92
ORIGINAL_ARTICLE
Application of Random Forest Regression in the Prediction of Ultimate Bearing Capacity of Strip Footing Resting on Dense Sand Overlying Loose Sand Deposit
The paper presents the prediction of the ultimate bearing capacity of the strip footing resting on layered soil (dense sand overlying loose sand) using random forest regression (RFR). In this study, 181 data collected from literature were used. 71 % of the total data was randomly selected for training the model and the rest of the data were utilized for the testing purpose. The various input parameters were friction angle of the dense sand layer (f1), friction angle of the loose sand layer (f2), unit weight of the dense sand layer (g1), unit weight of the loose sand layer (g2), ratio of the thickness of the dense sand layer below base of the footing to the width of footing (H/B), ratio of the depth of the footing to the width of the footing (D/B) and (H+D)/B. Ultimate bearing capacity was the output in this study. Performance measures were used in order to make the comparison with the artificial neural network (ANN) and M5P model tree. The result of this study revealed that the performance of the RFR was superior to M5P and ANN. The results of the sensitivity analysis reveals that the unit weight and the friction angle of the loose sand layer were the most important parameters affecting the output ultimate bearing capacity of the strip footing resting on the layered soils.
https://www.jsoftcivil.com/article_89975_f43eb32c03cc3ca30b3cd7f299cd6a1b.pdf
2019-10-01
28
40
10.22115/scce.2019.137910.1080
Random forest regression
Ultimate bearing capacity
Layered sand
M5P model tree
Artificial Neural Network
Sensitivity analysis
Rakesh
Dutta
rakeshkdutta@yahoo.com
1
Professor, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
AUTHOR
Tammineni
Gnananandarao
anandrcwing@gmail.com
2
Research Scholar, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
LEAD_AUTHOR
Ajay
Sharma
ajsharma.sharma782@gmail.com
3
PG Student, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
AUTHOR
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[12] Yin J-H, Wang Y-J, Selvadurai APS. Influence of Nonassociativity on the Bearing Capacity of a Strip Footing. J Geotech Geoenvironmental Eng 2001;127:985–9. doi:10.1061/(ASCE)1090-0241(2001)127:11(985).
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[13] Zhu M. Bearing capacity of strip footings on two-layer clay soil by finite element method. Proc. ABAQUS Users’ Conf., vol. 777, 2004, p. 787.
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[14] Zhu M, Michalowski RL. Shape Factors for Limit Loads on Square and Rectangular Footings. J Geotech Geoenvironmental Eng 2005;131:223–31. doi:10.1061/(ASCE)1090-0241(2005)131:2(223).
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[15] Szypcio Z, Dołżyk-Szypcio K. The Bearing Capacity of Layered Subsoil. vol. XXVIII. 2006.
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[16] Kumar J, Kouzer KM. Effect of Footing Roughness on Bearing Capacity Factor Nγ. J Geotech Geoenvironmental Eng 2007;133:502–11. doi:10.1061/(ASCE)1090-0241(2007)133:5(502).
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[17] Nazir R, Momeni E, Marsono K, Maizir H. An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand. J Teknol 2015;72. doi:10.11113/jt.v72.4004.
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[18] Kalinli A, Acar MC, Gündüz Z. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng Geol 2011;117:29–38. doi:10.1016/j.enggeo.2010.10.002.
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[19] Ornek M. Estimation of ultimate loads of eccentric-inclined loaded strip footings rested on sandy soils. Neural Comput Appl 2014;25:39–54. doi:10.1007/s00521-013-1444-5.
19
[20] Soleimanbeigi A, Hataf N. Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks. Geosynth Int 2005;12:321–32.
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[21] Dutta RK, Dutta K, Jeevanandham S. Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network. Int J Geosynth Gr Eng 2015;1:11. doi:10.1007/s40891-015-0013-7.
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[22] Kuo YL, Jaksa MB, Lyamin AV, Kaggwa WS. ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Comput Geotech 2009;36:503–16. doi:10.1016/j.compgeo.2008.07.002.
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[23] Puri N, Prasad HD, Jain A. Prediction of Geotechnical Parameters Using Machine Learning Techniques. Procedia Comput Sci 2018;125:509–17. doi:10.1016/j.procs.2017.12.066.
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[24] Dutta RK, Rani R, Rao T. Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks. Soft Comput Civ Eng 2018:34–46. doi:10.22115/scce.2018.133742.1066.
24
[25] Gnananandarao T, Dutta RK, Khatri VN. Artificial Neural Networks Based Bearing Capacity Prediction for Square Footing Resting on Confined Sand. Indian Geotech. Conf. 14-16 December, IIT Guwahati, Assam, India, 2017.
25
[26] Gnananandarao T, Dutta RK, Khatri VN. Application of Artificial Neural Network to Predict the Settlement of Shallow Foundations on Cohesionless Soils, 2019, p. 51–8. doi:10.1007/978-981-13-0368-5_6.
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[27] Dutta RK, Dutta K, Kumar S. S. Prediction of horizontal stress in underground excavations using artificial neural networks. Int J Civ Eng Appl 2016;6.
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[28] Dutta RK, Gupta R. Prediction of unsoaked and soaked California bearing ratio from index properties of soil using artificial neural networks. Int J Civ Eng Appl 2016;6.
28
[29] Pal M, Deswal S. Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network. J Geotech Geoenvironmental Eng 2008;134:1021–4. doi:10.1061/(ASCE)1090-0241(2008)134:7(1021).
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[30] Samui P. Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 2008;35:419–27. doi:10.1016/j.compgeo.2007.06.014.
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[31] Liu H, Xie D, Wu W. Soil water content forecasting by ANN and SVM hybrid architecture. Environ Monit Assess 2008;143:187–93. doi:10.1007/s10661-007-9967-9.
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42
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43
ORIGINAL_ARTICLE
Life Cycle Cost GA Optimization of Repaired Reinforced Concrete Structures Located in a Marine Environment
Life-Cycle-Cost (LCC) analysis of corroded structures located in corrosive marine environments considers the time-dependent resistance and loading affect, and repair and maintenance scenarios applied during life time of these structures. Finding the optimum repair and maintenance scenario for a corroded reinforced concrete (RC) structure is a significant process to select a repair and maintenance scenario with minimum LCC and maximum service lifetime. For this purpose, a finite element (FE) model is applied to assess the time-dependent capacity of corroded RC circular column using nonlinear analysis. In corrosion initiation phase, empirical chloride diffusion and surface chloride concentration models obtained for silica fume RC under long-term exposure in splash zone of Bandar-Abbas coasts, located in south side of Iran, and in corrosion propagation phase, empirical corrosion current density model for splash zone of a marine environment in literature is used for modeling of corrosion process. In this analysis, the influence of a number of repair or rehabilitation scenarios on the performance of a corroded circular RC column due to chloride-induced corrosion, including five different concrete surface coatings used on the external surface of concrete, four different increasing concrete cover thickness and using the new longitudinal and horizontal reinforcements after the initial cracking of concrete cover are investigated. These 11 different scenarios with considering a scenario without any repair are optimized by Genetic Algorithm (GA) based on minimum LCC cost and 40 years failure time in terms of corrosion.
https://www.jsoftcivil.com/article_100821_91caeaa890e29882e7903e1f490b2554.pdf
2019-10-01
41
50
10.22115/scce.2020.212823.1149
life cycle cost
Genetic Algorithm
Circular RC Column
Marine environment
Repair and Maintenance
corrosion
Atiye
Farahani
afarahani@tafreshu.ac.ir
1
Assistant Professor, Department of Civil Engineering, Tafresh University, Tafresh, Iran
LEAD_AUTHOR
[1] Xie J, Hu R. Experimental study on rehabilitation of corrosion-damaged reinforced concrete beams with carbon fiber reinforced polymer. Constr Build Mater 2013;38:708–16. doi:10.1016/j.conbuildmat.2012.09.023.
1
[2] Vaysburd AM, Emmons PH. Corrosion inhibitors and other protective systems in concrete repair: concepts or misconcepts. Cem Concr Compos 2004;26:255–63. doi:10.1016/S0958-9465(03)00044-1.
2
[3] Alizadeh R, Ghods P, Chini M, Hoseini M, Ghalibafian M, Shekarchi M. Effect of Curing Conditions on the Service Life Design of RC Structures in the Persian Gulf Region. J Mater Civ Eng 2008;20:2–8. doi:10.1061/(ASCE)0899-1561(2008)20:1(2).
3
[4] Guzmán S, Gálvez JC. Modelling of concrete cover cracking due to non-uniform corrosion of reinforcing steel. Constr Build Mater 2017;155:1063–71. doi:10.1016/j.conbuildmat.2017.08.082.
4
[5] Ehlen MA. Life-365TM Service Life Prediction ModelTM and computer program for predicting the service life and life-cycle cost of reinforced concrete exposed to chlorides. Manual of Life-365TM Version 2.1, Produced by the Life-365TM Consortium II. 2012.
5
[6] de Vera G, Climent MA, Viqueira E, Antón C, Andrade C. A test method for measuring chloride diffusion coefficients through partially saturated concrete. Part II: The instantaneous plane source diffusion case with chloride binding consideration. Cem Concr Res 2007;37:714–24. doi:10.1016/j.cemconres.2007.01.008.
6
[7] Farahani A, Shekarchi M. Time-Dependent Structural Behavior of Repaired Corroded RC Columns Located in a Marine Site. J Rehabil Civ Eng 2020;8:40–9.
7
[8] Khaghanpour R, Dousti A, Shekarchi M. Prediction of Cover Thickness Based on Long-Term Chloride Penetration in a Marine Environment. J Perform Constr Facil 2017;31:04016070. doi:10.1061/(ASCE)CF.1943-5509.0000931.
8
[9] Tadayon MH, Shekarchi M, Tadayon M. Long-term field study of chloride ingress in concretes containing pozzolans exposed to severe marine tidal zone. Constr Build Mater 2016;123:611–6. doi:10.1016/j.conbuildmat.2016.07.074.
9
[10] Cheewaket T, Jaturapitakkul C, Chalee W. Effect of Fly Ash on Chloride Penetration and Compressive Strength of Reclycled and Natural Aggregate Concrete under 5-year Exposure in Marine Environment. J King Mongkut’s Univ Technol North Bangkok 2019;29:112–23.
10
[11] Ožbolt J, Oršanić F, Balabanić G. Modelling processes related to corrosion of reinforcement in concrete: coupled 3D finite element model. Struct Infrastruct Eng 2017;13:135–46. doi:10.1080/15732479.2016.1198400.
11
[12] Otieno M, Beushausen H, Alexander M. Resistivity-based chloride-induced corrosion rate prediction models and hypothetical framework for interpretation of resistivity measurements in cracked RC structures. Mater Struct 2016;49:2349–66. doi:10.1617/s11527-015-0653-z.
12
[13] Kong Q, Gong G, Yang J, Song X. The corrosion rate of reinforcement in chloride contaminated concrete. Low Temp Archit Technol 2006;111:1–2.
13
[14] Vu KAT, Stewart MG. Structural reliability of concrete bridges including improved chloride-induced corrosion models. Struct Saf 2000;22:313–33. doi:10.1016/S0167-4730(00)00018-7.
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[15] Cho SH, Chung L, Roh Y-S. Estimation of Rebar Corrosion Rage in Reinforced Concrete Structure. Corros Rev 2005;23:329–54.
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[16] Cheng M-Y, Chiu Y-F, Chiu C-K, Prayogo D, Wu Y-W, Hsu Z-L, et al. Risk-based maintenance strategy for deteriorating bridges using a hybrid computational intelligence technique: a case study. Struct Infrastruct Eng 2019;15:334–50. doi:10.1080/15732479.2018.1547767.
16
[17] Khanzadeh Moradllo M, Shekarchi M, Hoseini M. Time-dependent performance of concrete surface coatings in tidal zone of marine environment. Constr Build Mater 2012;30:198–205. doi:10.1016/j.conbuildmat.2011.11.044.
17
[18] Yanaka M, Hooman Ghasemi S, Nowak AS. Reliability-based and life-cycle cost-oriented design recommendations for prestressed concrete bridge girders. Struct Concr 2016;17:836–47. doi:10.1002/suco.201500197.
18
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19
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20
[21] Farahani A, Taghaddos H, Shekarchi M. Influence of Repair on Corrosion Failure Modes of Square RC Columns Located in Tidal Zone. J Perform Constr Facil 2020;Accepted.
21
[22] Afsar Dizaj E, Madandoust R, Kashani MM. Exploring the impact of chloride-induced corrosion on seismic damage limit states and residual capacity of reinforced concrete structures. Struct Infrastruct Eng 2018;14:714–29. doi:10.1080/15732479.2017.1359631.
22
[23] Hazus -MH MR5. Advanced Engineering Building Module (AEBM). Department of Homeland, Security Federal Emergency Management Agency, Mitigation Division, Washington. n.d.
23
[24] Farahani A, Taghaddos H, Shekarchi M. Prediction of long-term chloride diffusion in silica fume concrete in a marine environment. Cem Concr Compos 2015;59:10–7. doi:10.1016/j.cemconcomp.2015.03.006.
24
[25] Tadayon MM. Modeling Reinforcement Corrosion Initiation & Propagation for Life Time Estimation of Concrete Structures in Persian Gulf Environment. Ph.D. Thesis, University of Tehran, School of Civil Engineering, Tehran, Iran., 2018.
25
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[27] Liu Y, Weyers RE. Modeling the time-to-corrosion cracking in chloride contaminated reinforced concrete structures. ACI Mater J n.d.;95:675–81.
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[28] Val D V. Factors affecting life-cycle cost analysis of RC structures in chloride contaminated environments. J Infrastruct Syst 2007;13:135–43. doi:10.1061/(ASCE)1076-0342(2007)13:2(135).
28
ORIGINAL_ARTICLE
Selecting the Suitable Tunnel Supporting System Using an Integrated Decision Support System, (Case Study: Dolaei Tunnel of Touyserkan, Iran)
The main goal of this study is the selection of an appropriate tunnel supporting system according to the combination of FDAHP method (Fuzzy Delphi Analytic Hierarchy Process) and ELECTRE (Elimination and Choice Expressing Reality) technique. This integrated decision support system provides useful support for selecting a tunnel supporting system. The weights of the criteria was determined by FDAHP method, and a suitable tunnel supporting system for Dolaei tunnel of Touyserkan in Iran was determined by the ELECTRE. The study was supported by the results obtained from a questionnaire carried out to understand the opinions of experts in this subject. According to surveys in this regard, six significant criteria and five alternatives such as reinforced shotcrete, metal frames, concrete prefabricated segments, in situ reinforced concrete implementation, rock bolt and reinforced shotcrete implementation have been examined. The obtained results showed that the rock bolt with reinforced shotcrete supporting system is the most suitable.
https://www.jsoftcivil.com/article_101847_564acdc827639438a714f62b6be3289e.pdf
2019-10-01
51
66
10.22115/scce.2020.212995.1150
Supporting systems
FDAHP
Electre
integrated decision
Dolaei Tunnel
Sina
Shaffiee Haghshenas
sina.shaffieehaghshenas@unical.it
1
M.Sc. Student of Civil Engineering, Department of Civil Engineering, University of Calabria, Rende, Italy
AUTHOR
Reza
Mikaeil
reza.mikaeil@uut.ac.ir
2
Associate Professor of Mining Engineering, Faculty of Mining and Metallurgical Engineering, Urmia University of Technology (UUT), Urmia, Iran
LEAD_AUTHOR
Mehdi
Abdollahi Kamran
kamran.m@uut.ac.ir
3
Assistant Professor of Industrial Engineering, Faculty of Industrial Engineering, Urmia University of Technology (UUT), Urmia, Iran
AUTHOR
Sami
Shaffiee Haghshenas
sami.shaffiee@gmail.com
4
M.Sc. Student of Civil Engineering, Department of Civil Engineering, University of Calabria, Rende, Italy
AUTHOR
Hojjat
Hosseinzadeh Gharehgheshlagh
h.hoseynzade@uut.ac.ir
5
Assistant Professor of Mining Engineering, Faculty of Mining and Metallurgical Engineering, Urmia University of Technology (UUT), Urmia, Iran
AUTHOR
[1] Haghshenas SS, Haghshenas SS, Barmal M, Farzan N. Utilization of soft computing for risk assessment of a tunneling project using geological units. Civ Eng J 2016;2:358–64.
1
[2] Salemi A, Mikaeil R, Haghshenas SS. Integration of Finite Difference Method and Genetic Algorithm to Seismic analysis of Circular Shallow Tunnels (Case Study: Tabriz Urban Railway Tunnels). KSCE J Civ Eng 2018;22:1978–90. doi:10.1007/s12205-017-2039-y.
2
[3] Haghshenas SS, Haghshenas SS, Mikaeil R, Ardalan T, Sedaghati Z, Kazemzadeh Heris P. Selection of an Appropriate Tunnel Boring Machine Using TOPSIS-FDAHP Method (Case Study: Line 7 of Tehran Subway, East-West Section). Electron J Geotech Eng 2017;22:4047–62.
3
[4] Mikaeil R, Shaffiee Haghshenas S, Shirvand Y, Valizadeh Hasanluy M, Roshanaei V. Risk Assessment of Geological Hazards in a Tunneling Project Using Harmony Search Algorithm (Case Study: Ardabil-Mianeh Railway Tunnel). Civ Eng J 2016;2:546–54. doi:10.28991/cej-2016-00000057.
4
[5] Mikaeil R, Shaffiee Haghshenas S, Sedaghati Z. Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel). Nat Hazards 2019;97:1099–113. doi:10.1007/s11069-019-03688-z.
5
[6] Mikaeil R, Bakhshinezhad H, Haghshenas SS, Ataei M. STABILITY ANALYSIS OF TUNNEL SUPPORT SYSTEMS USING NUMERICAL AND INTELLIGENT SIMULATIONS (CASE STUDY: KOUHIN TUNNEL OF QAZVIN-RASHT RAILWAY). Rud Zb 2019;34:1–11. doi:10.17794/rgn.2019.2.1.
6
[7] Mikaeil R, Beigmohammadi M, Bakhtavar E, Haghshenas SS. Assessment of risks of tunneling project in Iran using artificial bee colony algorithm. SN Appl Sci 2019;1:1711. doi:10.1007/s42452-019-1749-9.
7
[8] Esmailzadeh A, Shirzad PJ, Haghshenas SS. Technical analysis of collapse in tunnel excavation and suggestion of preventing appropriate applicable methods (case study: sardasht dam second diversion tunnel). Civ Eng J 2017;3:682–9.
8
[9] Tehrani K. Geology of Iran. Publications of Payam Noor University, Tehran, Iran 1996.
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[10] Nazari S. Dolaei tunnel geological report. Shahied Rajaeei Institute. Tehran, Iran. 2003.
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[11] Nazari SM., Amiri R, Kolivand A. Evaluation of various factors in the loss Dolaei Access Tunnel of Tuyserkan City. 6th Natl Conf Tunnel Tehran, Iran 2004.
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[14] Wang T-C, Chen Y-H. Applying consistent fuzzy preference relations to partnership selection. Omega 2007;35:384–8. doi:10.1016/j.omega.2005.07.007.
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[15] Deng H. Multicriteria analysis with fuzzy pairwise comparison. Int J Approx Reason 1999;21:215–31. doi:10.1016/S0888-613X(99)00025-0.
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[16] Analysis of Protection of Body Slope in the Rockfill Reservoir Dams on the Basis of Fuzzy Logic. Proc 4th Int Jt Conf Comput Intell, SciTePress - Science and and Technology Publications; 2012, p. 367–73. doi:10.5220/0004153803670373.
16
[17] Shafiee Haghshenas S, Mikaeil R, Shaffiee Haghshenas S, Zare Naghadehi M, Sirati Moghadam P. Fuzzy and Classical MCDM Techniques to Rank the Slope Stabilization Methods in a Rock-Fill Reservoir Dam. Civ Eng J 2017;3:382–94. doi:10.28991/cej-2017-00000099.
17
[18] Mikaeil R, Haghshenas SS, Hoseinie SH. Rock Penetrability Classification Using Artificial Bee Colony (ABC) Algorithm and Self-Organizing Map. Geotech Geol Eng 2017. doi:10.1007/s10706-017-0394-6.
18
[19] Mikaeil R, Haghshenas SS, Haghshenas SS, Ataei M. Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Comput Appl 2018;29:283–92. doi:10.1007/s00521-016-2557-4.
19
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[22] Shirani Faradonbeh R, Shaffiee Haghshenas S, Taheri A, Mikaeil R. Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects. Neural Comput Appl 2019. doi:10.1007/s00521-019-04353-z.
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43
ORIGINAL_ARTICLE
Predicting the Earthquake Magnitude along Zagros Fault Using Time Series and Ensemble Model
Predicting the earthquake magnitude is a complex problem, which has been carried out in recent years. The machine learning, geophysical, and regression methods were used to predict earthquake magnitude in literature. Iran is located in a highly seismically active area; thus, earthquake prediction is considered as a great demand there. In this study, two time series algorithms are utilized to predict the magnitude of the earthquake based on previous earthquakes. These models are autoregressive conditional heteroscedasticity (GARCH), autoregressive integrated moving average (ARIMA), and the combination of ARIMA and GARCH by multiple linear regression (MLR) technique (model 3). The 9017 events are used to train and predict earthquake magnitude. On the other hand, 6188 events are applied for training models, and then 2829 events are utilized for testing it. The statistical parameters, such as correlation coefficient, root mean square error (RMSE), normalized square error (NMSE), and fractional bias, are calculated to evaluate the accuracy of each model. The results demonstrate that the ARIMA and model 3 can predict future earthquake magnitude better than other models.
https://www.jsoftcivil.com/article_101846_6965f433c0520d92f4af5bc0f3a44e98.pdf
2019-10-01
67
77
10.22115/scce.2020.213197.1152
Earthquake Magnitude
time series
statistical parameters
prediction
ensemble technique
Aydin
Shishegaran
aydinshishegaranam@gmail.com
1
Ph.D. Candidate, Environmental Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Hamed
Taghavizade
hamed.taghavizade@iiees.ac.ir
2
Research Assistant, Geotechnical Earthquake Engineering, International Institute of Earthquake Engineering and Seismology, Tehran, Iran
AUTHOR
Alireza
Bigdeli
alireza.bigdeli94@gmail.com
3
Master of Science Student, Structural Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Arshia
Shishegaran
shishegaranarshia@gmail.com
4
Master of Science Student, Structural Engineering, School of Civil Engineering, Islamic Azad University, Tehran, Iran
AUTHOR
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[3] Špičák A, Vaněk J. Earthquake swarms reveal submarine magma unrest induced by distant mega-earthquakes: Andaman Sea region. J Asian Earth Sci 2016;116:155–63. doi:10.1016/j.jseaes.2015.11.017.
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[4] Verdugo R, González J. Liquefaction-induced ground damages during the 2010 Chile earthquake. Soil Dyn Earthq Eng 2015;79:280–95. doi:10.1016/j.soildyn.2015.04.016.
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[7] Fazendeiro Sá L, Morales‐Esteban A, Durand Neyra P. A Seismic Risk Simulator for Iberia. Bull Seismol Soc Am 2016;106:1198–209. doi:10.1785/0120150195.
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[9] Rouet-Leduc B, Hulbert C, Lubbers N, Barros K, Humphreys CJ, Johnson PA. Machine Learning Predicts Laboratory Earthquakes. Geophys Res Lett 2017;44:9276–82. doi:10.1002/2017GL074677.
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[10] Asencio–Cortés G, Morales–Esteban A, Shang X, Martínez–Álvarez F. Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput Geosci 2018;115:198–210. doi:10.1016/j.cageo.2017.10.011.
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[12] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.
12
[13] Shishegaran A, Ghasemi MR, Varaee H. Performance of a novel bent-up bars system not interacting with concrete. Front Struct Civ Eng 2019;13:1301–15. doi:10.1007/s11709-019-0552-4.
13
[14] Shishegaran A, Khalili MR, Karami B, Rabczuk T, Shishegaran A. Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. Int J Impact Eng 2020;139:103527. doi:10.1016/j.ijimpeng.2020.103527.
14
[15] Shishegaran A, Daneshpajoh F, Taghavizade H, Mirvalad S. Developing conductive concrete containing wire rope and steel powder wastes for route deicing. Constr Build Mater 2020;232:117184. doi:10.1016/j.conbuildmat.2019.117184.
15
[16] Mohammadkhani MR, Shishegaran A, Shokrollahi B. Forecasting probable maximum precipitation using innovative algorithm to estimate atmosphere precipitable water vapor. Math Model Eng 2019;5:90–6. doi:10.21595/mme.2019.20935.
16
[17] Shishegaran A, Amiri A, Jafari MA. Seismic performance of box-plate, box-plate with UNP, box-plate with L-plate and ordinary rigid beam-to-column moment connections. J Vibroengineering 2018;20:1470–87. doi:10.21595/jve.2017.18716.
17
[18] Shishegaran A, Rahimi S, Darabi H. Introducing box-plate beam-to-column moment connections. Vibroengineering PROCEDIA 2017;11:200–4. doi:10.21595/vp.2017.18548.
18
[19] Reza Ghasemi M, Shishegaran A. Role of slanted reinforcement on bending capacity SS beams. Vibroengineering PROCEDIA 2017;11:195–9. doi:10.21595/vp.2017.18544.
19
[20] Naderpour H, Fakharian P. A synthesis of peak picking method and wavelet packet transform for structural modal identification. KSCE J Civ Eng 2016;20:2859–67. doi:10.1007/s12205-016-0523-4.
20
[21] Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84. doi:10.1016/j.compstruct.2019.02.048.
21
[22] Naderpour H, Fakharian P. Predicting the torsional strength of reinforced concrete beams strengthened with FRP sheets in terms of artificial neural networks. J Struct Constr Eng 2018;5:20–35. doi:10.22065/JSCE.2017.70668.1023.
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27
ORIGINAL_ARTICLE
GIS 3D and Science of Augmented Reality: Modeling a 3D Geospatial Environment
The objective of this paper is to integrate all the 3D data into a Geographic Information System (GIS), from *.skp files that it modeled by applying augmented reality (AR). The application of the RA to a 3D model integrated into the GIS will be a valuable means of communication for the enhancement of our learning environment. Accessible to all, including those who cannot visit the site, it allows discovering for example ruins in a pedagogical and relevant way. From an architectural point of view, the 3D model provides an overview and a perspective on the constitution of the environment, which a 2D document can hardly offer. 3D navigation and the integration of 2D data into the model make it possible to analyze the remains in another way, contributing to the faster establishment of new hypotheses. Complementary to the other methods already exploited in geology, the analysis by 3D vision is, for the scientists, a non-negligible gain of time which they can thus devote to the more in-depth study of certain hypotheses put aside.
https://www.jsoftcivil.com/article_103747_04b050da859a88eb44e81daaa112fed6.pdf
2019-10-01
78
87
10.22115/scce.2020.212254.1148
3D
GIS
Augmented reality
2D
Adel
Fridhi
adel.fridhi2014@gmail.com
1
National Engineers School of Tunis, LRSITI (ENIT), Tunisia
LEAD_AUTHOR
Ali
Frihida
ali.frihida@enit.rnu.tn
2
National Engineers School of Tunis, LRSITI (ENIT), Tunisia
AUTHOR
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1
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12