ORIGINAL_ARTICLE
Neural Network Based Model to Estimate Dynamic Modulus E* for Mixtures in Costa Rica
Several dynamic modulus (E*) predictive models of asphalt mixtures have been developed as an alternative to laboratory testing. The 1999 I-37A Witczak equation is one of the most commonly used alternatives. This equation is based on mixtures laboratory results in the U.S. In Latin American countries there are significant differences in material properties, traffic information, and environmental conditions compared to the U.S.; therefore, there is a limitation is the use of this equation using local conditions. The National Laboratory of Materials and Structural Models at the University of Costa Rica (LanammeUCR) has previously performed a local calibration of this equation based on results from different types of Costa Rican mixtures. However, there was still room for improvement using advanced regression techniques such as neural networks (NN). The objective of this study was to develop an improved and more effective dynamic modulus regression model for mixtures in Costa Rica using Neural Networks. Results indicated that the new and improved model based on neural networks (E* NN-model) not only met the model adequacy checking criteria but also exhibited the best goodness of fit parameters and the lowest overall bias.
http://www.jsoftcivil.com/article_89875_06242302b0557e90e5e9f7a540e8a9ad.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
1
15
10.22115/scce.2019.188006.1110
Neural Network
dynamic modulus
Asphalt mixtures
Pavements
Master Curves
Fabricio
Leiva-Villacorta
leivafa@auburn.edu
true
1
National Center for Asphalt Technology, Auburn University, Auburn, United States
National Center for Asphalt Technology, Auburn University, Auburn, United States
National Center for Asphalt Technology, Auburn University, Auburn, United States
LEAD_AUTHOR
Adriana
Vargas-Nordcbeck
vargaad@auburn.edu
true
2
National Center for Asphalt Technology, Auburn University, Auburn, United States
National Center for Asphalt Technology, Auburn University, Auburn, United States
National Center for Asphalt Technology, Auburn University, Auburn, United States
AUTHOR
[1] ARA I. ERES Consultants Division. Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures. Final report, NCHRP Project 1-37A. Transportation Research Board of the National Academies, Washington, D.C. 2004.
1
[2] Clyne TR, Li X, Marastenau MO, Skok EL. Dynamic and Resilient Modulus of Mn/DOT Asphalt Mixtures.“ Final Report MN/RC – 2003-09. University of Minnesota, Minneapolis, MN. 2003.
2
[3] Birgisson B, Sholar G, Roque R. Evaluation of a Predicted Dynamic Modulus for Florida Mixtures. Transp Res Rec J Transp Res Board 2005;1929:200–7. doi:10.1177/0361198105192900124.
3
[4] Kim YR, King M, Momen M. Typical dynamic moduli values of hot mix asphalt in North Carolina and their prediction. Transp. Res. Board 84th Annu. Meet. Compend. Pap., 2005, p. 5–2568.
4
[5] Schwartz CW. Evaluation of the Witczak dynamic modulus prediction model. Proc. 84th Annu. Meet. Transp. Res. Board, Washington, DC, 2005.
5
[6] Tran N, Hall K. Evaluating the predictive equation in determining dynamic moduli of typical asphalt mixtures used in Arkansas 2005.
6
[7] Robbins MM, Timm DH. Evaluation of Dynamic Modulus Predictive Equations for Southeastern United States Asphalt Mixtures. Transportation Research Record. J Transp Res Board 2011;72:122–9.
7
[8] Christensen Jr DW, Pellinen T, Bonaquist RF. Hirsch model for estimating the modulus of asphalt concrete. J Assoc Asph Paving Technol 2003;72.
8
[9] Singh D, Zaman M, Commuri S. Evaluation of Predictive Models for Estimating Dynamic Modulus of HMA Mixtures Used in Oklahoma 2010.
9
[10] El-Badawy S, Bayomy F, Awed A. Performance of MEPDG dynamic modulus predictive models for asphalt concrete mixtures: local calibration for Idaho. J Mater Civ Eng 2012;24:1412–21.
10
[11] Loria LG, Badilla G, Jimenez Acuna M, Elizondo F, Aguiar-Moya JP. Experiences in the Characterization of Materials Used in the Calibration of the AASHTO’Mechanistic-Empirical Pavement Design Guide (MEPDG) for Flexible Pavement for Costa Rica. 2011.
11
[12] Far MSS, Underwood BS, Ranjithan SR, Kim YR, Jackson N. Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete. Transp Res Rec J Transp Res Board 2009;2127:173–86. doi:10.3141/2127-20.
12
[13] Ceylan H, Schwartz CW, Kim S, Gopalakrishnan K. Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt. J Mater Civ Eng 2009;21:286–93. doi:10.1061/(ASCE)0899-1561(2009)21:6(286).
13
[14] Priddy KL, Keller PE. Artificial neural networks: an introduction. vol. 68. SPIE press; 2005.
14
ORIGINAL_ARTICLE
Evolutionary Algorithm Performance Evaluation in Project Time-Cost Optimization
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.
http://www.jsoftcivil.com/article_89544_996b03d57a072a2560d4b5cdbced6621.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
16
29
10.22115/scce.2019.155434.1091
Evolutionary Algorithm
optimization
Project scheduling
Time-Cost Trade-Off
Construction management
Athanasios
Chassiakos
a.chassiakos@upatras.gr
true
1
Associate Professor, Department of Civil Engineering, University of Patras, Patras, Greece
Associate Professor, Department of Civil Engineering, University of Patras, Patras, Greece
Associate Professor, Department of Civil Engineering, University of Patras, Patras, Greece
LEAD_AUTHOR
George
Rempis
georgerempis@gmail.com
true
2
Civil Engineer, Graduate, Department of Civil Engineering, University of Patras, Patras, Greece
Civil Engineer, Graduate, Department of Civil Engineering, University of Patras, Patras, Greece
Civil Engineer, Graduate, Department of Civil Engineering, University of Patras, Patras, Greece
AUTHOR
[1] Liu L, Burns SA, Feng C-W. Construction Time-Cost Trade-Off Analysis Using LP/IP Hybrid Method. J Constr Eng Manag 1995;121:446–54. doi:10.1061/(ASCE)0733-9364(1995)121:4(446).
1
[2] Sakellaropoulos S, Chassiakos AP. Project time–cost analysis under generalised precedence relations. Adv Eng Softw 2004;35:715–24. doi:10.1016/j.advengsoft.2004.03.017.
2
[3] Klanšek U, Pšunder M. MINLP optimization model for the nonlinear discrete time–cost trade-off problem. Adv Eng Softw 2012;48:6–16. doi:10.1016/j.advengsoft.2012.01.006.
3
[4] De P, James Dunne E, Ghosh JB, Wells CE. The discrete time-cost tradeoff problem revisited. Eur J Oper Res 1995;81:225–38. doi:10.1016/0377-2217(94)00187-H.
4
[5] Akkan C, Drexl A, Kimms A. Network decomposition-based benchmark results for the discrete time–cost tradeoff problem. Eur J Oper Res 2005;165:339–58. doi:10.1016/j.ejor.2004.04.006.
5
[6] Hazır Ö, Haouari M, Erel E. Discrete time/cost trade-off problem: A decomposition-based solution algorithm for the budget version. Comput Oper Res 2010;37:649–55. doi:10.1016/j.cor.2009.06.009.
6
[7] Feng C-W, Liu L, Burns SA. Using Genetic Algorithms to Solve Construction Time-Cost Trade-Off Problems. J Comput Civ Eng 1997;11:184–9. doi:10.1061/(ASCE)0887-3801(1997)11:3(184).
7
[8] Hegazy T. Optimization of construction time-cost trade-off analysis using genetic algorithms. Can J Civ Eng 1999;26:685–97. doi:10.1139/l99-031.
8
[9] Li H, Love P. Using Improved Genetic Algorithms to Facilitate Time-Cost Optimization. J Constr Eng Manag 1997;123:233–7. doi:10.1061/(ASCE)0733-9364(1997)123:3(233).
9
[10] Li H, Cao J-N, Love PED. Using Machine Learning and GA to Solve Time-Cost Trade-Off Problems. J Constr Eng Manag 1999;125:347–53. doi:10.1061/(ASCE)0733-9364(1999)125:5(347).
10
[11] Zheng DXM, Ng ST, Kumaraswamy MM. Applying a Genetic Algorithm-Based Multiobjective Approach for Time-Cost Optimization. J Constr Eng Manag 2004;130:168–76. doi:10.1061/(ASCE)0733-9364(2004)130:2(168).
11
[12] Zheng DXM, Ng ST, Kumaraswamy MM. Applying Pareto Ranking and Niche Formation to Genetic Algorithm-Based Multiobjective Time–Cost Optimization. J Constr Eng Manag 2005;131:81–91. doi:10.1061/(ASCE)0733-9364(2005)131:1(81).
12
[13] Yang I-T. Using Elitist Particle Swarm Optimization to Facilitate Bicriterion Time-Cost Trade-Off Analysis. J Constr Eng Manag 2007;133:498–505. doi:10.1061/(ASCE)0733-9364(2007)133:7(498).
13
[14] Zhang H, Li H. Multi‐objective particle swarm optimization for construction time‐cost tradeoff problems. Constr Manag Econ 2010;28:75–88. doi:10.1080/01446190903406170.
14
[15] Ng ST, Zhang Y. Optimizing Construction Time and Cost Using Ant Colony Optimization Approach. J Constr Eng Manag 2008;134:721–8. doi:10.1061/(ASCE)0733-9364(2008)134:9(721).
15
[16] Xiong Y, Kuang Y. Applying an Ant Colony Optimization Algorithm-Based Multiobjective Approach for Time–Cost Trade-Off. J Constr Eng Manag 2008;134:153–6. doi:10.1061/(ASCE)0733-9364(2008)134:2(153).
16
[17] Afshar A, Ziaraty AK, Kaveh A, Sharifi F. Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization. J Constr Eng Manag 2009;135:668–74. doi:10.1061/(ASCE)0733-9364(2009)135:7(668).
17
[18] Sonmez R, Bettemir ÖH. A hybrid genetic algorithm for the discrete time–cost trade-off problem. Expert Syst Appl 2012;39:11428–34. doi:10.1016/j.eswa.2012.04.019.
18
[19] Elbeltagi E, Hegazy T, Grierson D. Comparison among five evolutionary-based optimization algorithms. Adv Eng Informatics 2005;19:43–53. doi:10.1016/j.aei.2005.01.004.
19
[20] xlOptimizer software, www.xloptimizer.com, 2015.
20
[21] Eiben AE, Smith JE. Introduction to evolutionary computing. vol. 53. Springer; 2003.
21
[22] Koumousis VK, Katsaras CP. A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans Evol Comput 2006;10:19–28. doi:10.1109/TEVC.2005.860765.
22
[23] Krishnakumar K. Micro-genetic algorithms for stationary and non-stationary function optimization. Intell. Control Adapt. Syst., vol. 1196, International Society for Optics and Photonics; 1990, p. 289–96.
23
[24] Kennedy J, Eberhart R. Particle swarm optimization. Proc. IEEE Int. Conf. neural networks (Perth, Aust., 1995, p. 1942–8.
24
[25] Price K, Storn RM, Lampinen JA. Differential evolution: a practical approach to global optimization. Springer Science & Business Media; 2006.
25
[26] Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 2008;8:687–97. doi:10.1016/j.asoc.2007.05.007.
26
ORIGINAL_ARTICLE
Application of Soft Computing Techniques in Predicting the Ultimate Bearing Capacity of Strip Footing Subjected to Eccentric Inclined Load and Resting on Sand
http://www.jsoftcivil.com/article_89749_3a9b7ac3e8ceb96dfb55aa55d17d2db4.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
30
42
10.22115/scce.2019.144535.1088
Ultimate bearing capacity
Reduction factor
Eccentrically inclined load
SVM RBF kernel
M5P model tree
Random forest regression
Rakesh
Dutta
rakeshkdutta@yahoo.com
true
1
Professor, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
Professor, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
Professor, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
AUTHOR
Tammineni
Gnanananda Rao
anandrcwing@gmail.com
true
2
Research Scholar, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
Research Scholar, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
Research Scholar, Department of Civil Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India
LEAD_AUTHOR
Vishwas
Khatri
vishuiisc@gmail.com
true
3
Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India
Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India
Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India
AUTHOR
[1] Patra C, Behara R, Sivakugan N, Das B. Ultimate bearing capacity of shallow strip foundation under eccentrically inclined load, Part II. Int J Geotech Eng 2012;6:507–14. doi:10.3328/IJGE.2012.06.04.507-514.
1
[2] 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.
2
[3] Taiebat H, Carter J. Bearing capacity of strip and circular foundations on undrained clay subjected to eccentric loads. vol. 52. 2002. doi:10.1680/geot.52.1.61.40828.
3
[4] Loukidis D, Chakraborty T, Salgado R. Bearing capacity of strip footings on purely frictional soil under eccentric and inclined loads. Can Geotech J 2008;45:768–87. doi:10.1139/T08-015.
4
[5] Meyerhof GG. Some Recent Research on the Bearing Capacity of Foundations. Can Geotech J 1963;1:16–26. doi:10.1139/t63-003.
5
[6] Prakash S, Saran S. Bearing capacity of eccentrically loaded footings. J Soil Mech Found Div 1971.
6
[7] Meyerhof GG, Hanna AM. Ultimate bearing capacity of foundations on layered soils under inclined load. Can Geotech J 1978;15:565–72. doi:10.1139/t78-060.
7
[8] Zheng G, Zhao J, Zhou H, Zhang T. Ultimate bearing capacity of strip footings on sand overlying clay under inclined loading. Comput Geotech 2019;106:266–73. doi:10.1016/j.compgeo.2018.11.003.
8
[9] Abyaneh S, Kennedy J, Maconochie A, Oliphant J. Capacity of Strip Foundations on Sand Overlying Clay Soils Under Planar Combined Loading. 28th Int Ocean Polar Eng Conf 2018:5.
9
[10] Patra C, Behara R, Sivakugan N, Das B. Ultimate bearing capacity of shallow strip foundation under eccentrically inclined load, Part I. Int J Geotech Eng 2012;6:343–52. doi:10.3328/IJGE.2012.06.03.343-352.
10
[11] Behera RN, Patra CR, Sivakugan N, Das BM. Prediction of ultimate bearing capacity of eccentrically inclined loaded strip footing by ANN: Part II. Int J Geotech Eng 2013;7:165–72. doi:10.1179/1938636213Z.00000000019.
11
[12] 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.
12
[13] 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.
13
[14] 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.
14
[15] 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.
15
[16] Gnananandarao T, Dutta RK, Khatri VN. Artificial Neural Networks Based Bearing Capacity Prediction for Square Footing Resting on Confined Sand n.d.
16
[17] 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.
17
[18] Dutta RK, Dutta K, Kumar S. S. Prediction of horizontal stress in underground excavations using artificial neural networks. vol. 5. 2016.
18
[19] Dutta RK, Gupta R. Prediction of unsoaked and soaked California bearing ratio from index properties of soil using artificial neural networks. vol. 5. 2016.
19
[20] Pal M, Singh NK, Tiwari NK. Pier scour modelling using random forest regression. ISH J Hydraul Eng 2013;19:69–75. doi:10.1080/09715010.2013.772763.
20
[21] Singh B, Sihag P, Singh K. Modelling of impact of water quality on infiltration rate of soil by random forest regression. Model Earth Syst Environ 2017;3:999–1004. doi:10.1007/s40808-017-0347-3.
21
[22] Perloff WH, Baron W. Soil mechanics: principles and applications. Ronald Press Co.; 1976.
22
[23] Vapnik V. The nature of statistical learning theory. Springer science & business media; 2013.
23
[24] Smola AJ. Regression estimation with support vector learning machines 1996.
24
[25] Quinlan JR. Learning with continuous classes.” In: Adams A, Sterling L (eds). Proc. 5th Aust. Jt. Conf. Artif. Intell. World Sci. Singapore, 1992, p. 343–8.
25
[26] Breiman L. RANDOM FORESTS--RANDOM FEATURES. Citeseer; 1999.
26
[27] Breiman L. Bagging predictors. Mach Learn 1996;24:123–40.
27
[28] Breiman L, Freidman JH, Olshen RA, Stone CJ. Classification and regression trees. Wadsworth, Monterey, CA. Classif Regres Trees Wadsworth, Monterey, CA 1984.
28
[29] Feller W. An introduction to probability theory and its applications. vol. 2. John Wiley & Sons; 2008.
29
[30] Hansen JB. A revised and extended formula for bearing capacity 1970.
30
[31] Vesic AS. Analysis of ultimate loads of shallow foundations. J Soil Mech Found Div 1973;99:45–73.
31
ORIGINAL_ARTICLE
Modelling of Concrete Compressive Strength Admixed with GGBFS Using Gene Expression Programming
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.
http://www.jsoftcivil.com/article_91665_ed4afd9d5a9158666ceacf4cd41596f4.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
43
53
10.22115/scce.2019.178214.1103
Concrete
Strength
GGBFS
GEP
O.
Akin
true
1
Postgraduate Student, Department of Civil Engineering, Ahmadu Bello University, Zaria, Nigeria
Postgraduate Student, Department of Civil Engineering, Ahmadu Bello University, Zaria, Nigeria
Postgraduate Student, Department of Civil Engineering, Ahmadu Bello University, Zaria, Nigeria
AUTHOR
Abejide
Samuel
abejideos@yahoo.com
true
2
Civil engineering Department, faculty of engineering, Ahmadu Bello University
Civil engineering Department, faculty of engineering, Ahmadu Bello University
Civil engineering Department, faculty of engineering, Ahmadu Bello University
LEAD_AUTHOR
ORIGINAL_ARTICLE
Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images
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.
http://www.jsoftcivil.com/article_95794_c4055a8743c2f21c00014fd4b94ed851.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
54
64
10.22115/scce.2019.196533.1119
Remote sensing, Geographic Information Systems (GIS)
artificial computation
Bathymetry
Amin
Jalilzadeh
amin.jalilzade12@gmail.com
true
1
M.Sc. Student in Geographic Information Systems, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
M.Sc. Student in Geographic Information Systems, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
M.Sc. Student in Geographic Information Systems, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
AUTHOR
Saeed
Behzadi
behzadi.saeed@gmail.com
true
2
Assistant Professor in Surveying Engineering, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
Assistant Professor in Surveying Engineering, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
Assistant Professor in Surveying Engineering, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
LEAD_AUTHOR
1. Cazenave, A., Satellite Altimetry☆, in Reference Module in Earth Systems and Environmental Sciences. 2018, Elsevier.
1
2. Ceyhun, Ö. and A. Yalçın, Remote sensing of water depths in shallow waters via artificial neural networks. Estuarine, Coastal and Shelf Science, 2010. 89(1): p. 89-96.
2
3. Elsahabi, M., O. Makboul, and A. Negm, Lake Nubia sediment capacity estimation based on satellite remotely sensed detected bathymetry. Procedia Manufacturing, 2018. 22: p. 567-574.
3
4. Lyzenga, D.R., Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 1978. 17(3): p. 379-383.
4
5. Dörnhöfer, K. and N. Oppelt, Remote sensing for lake research and monitoring – Recent advances. Ecological Indicators, 2016. 64: p. 105-122.
5
6. https://oceanservice.noaa.gov/facts/bathyuses.html.
6
7. https://sealevel.jpl.nasa.gov/overview/oceansurfacetopo/.
7
8. Duan, Z. and W.G.M. Bastiaanssen, Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sensing of Environment, 2013. 134: p. 403-416.
8
9. http://www2.csr.utexas.edu/grace/.
9
10. https://swot.jpl.nasa.gov/home.htm.
10
11. Calmant, S., F. Seyler, and J.F.J.S.i.g. Cretaux, Monitoring continental surface waters by satellite altimetry. 2008. 29(4-5): p. 247-269.
11
12. Li, C., et al., Multi-band remote sensing based retrieval model and 3D analysis of water depth in Hulun Lake, China. Mathematical and Computer Modelling, 2013. 58(3): p. 771-781.
12
13. Jay, S. and M. Guillaume, A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data. Remote Sensing of Environment, 2014. 147: p. 121-132.
13
14. Bramante, J.F., D.K. Raju, and S.J.I.J.o.R.S. Tsai Min, Derivation of bathymetry from multispectral imagery in the highly turbid waters of Singapore’s south islands: A comparative study. 2013. 34(6): p. 2070-2088.
14
15. Stumpf, R.P., et al., Determination of water depth with high‐resolution satellite imagery over variable bottom types. 2003. 48(1part2): p. 547-556.
15
16. Clark, R.K., T.H. Fay, and C.L.J.A.o. Walker, Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. 1987. 26(19): p. 4036_1-4038.
16
17. Karimi, N., et al., Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data. 2016. 30(14): p. 5003-5020.
17
18. Werdell, P.J., et al., An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Progress in Oceanography, 2018. 160: p. 186-212.
18
19. Traganos, D. and P. Reinartz, Mapping Mediterranean seagrasses with Sentinel-2 imagery. Marine Pollution Bulletin, 2018. 134: p. 197-209.
19
20. Zadeh, L.A., Fuzzy logic= computing with words. IEEE transactions on fuzzy systems, 1996. 4(2): p. 103-111.
20
21. Fang-fang, Z., et al., Comparative Analysis of Automatic Water Identification Method Based on Multispectral Remote Sensing. Procedia Environmental Sciences, 2011. 11: p. 1482-1487.
21
ORIGINAL_ARTICLE
Prediction of Flexural Strength of Concrete Produced by Using Pozzolanic Materials and Partly Replacing NFA by MS
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.
http://www.jsoftcivil.com/article_95795_23fd511845e9e97e80a838b56e1f0fc7.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
65
75
10.22115/scce.2019.197000.1121
pozzolanic materials
Manufactured sand
Flexural Strength
Artificial Neural Network
Kiran
Mane
kiranmane818@gmail.com
true
1
Ph.D. Research Scholar, S.D.M. College of Engineering and Technology, Dharwad, Karanataka, India
Ph.D. Research Scholar, S.D.M. College of Engineering and Technology, Dharwad, Karanataka, India
Ph.D. Research Scholar, S.D.M. College of Engineering and Technology, Dharwad, Karanataka, India
LEAD_AUTHOR
Dilip
Kulkarni
dilipkkulkarni@rediffmail.com
true
2
Professor, Department of Civil Engineering, S.D.M. College of Engineering and Technology, Dharwad, Karanataka, India
Professor, Department of Civil Engineering, S.D.M. College of Engineering and Technology, Dharwad, Karanataka, India
Professor, Department of Civil Engineering, S.D.M. College of Engineering and Technology, Dharwad, Karanataka, India
AUTHOR
K.
Prakash
kbprakash04@rediffmail.com
true
3
Principal, Govt. Engineering College, Haveri, Devagiri, Karanataka, India
Principal, Govt. Engineering College, Haveri, Devagiri, Karanataka, India
Principal, Govt. Engineering College, Haveri, Devagiri, Karanataka, India
AUTHOR
ORIGINAL_ARTICLE
Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) for Discrete and Continuous Size Optimization of 2D Truss Structures
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. 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. 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.
http://www.jsoftcivil.com/article_95950_4c2b24d7650ec7bb96b583258b12bc52.pdf
2019-04-01T11:23:20
2020-07-07T11:23:20
76
97
10.22115/scce.2019.195713.1117
Particle Swarm Optimization Algorithm
Genetic Algorithm
2D Truss Structures
discrete and continuous sizes
OpenSees
Mahmood
Akbari
makbari@kashanu.ac.ir
true
1
Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran
Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran
Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran
LEAD_AUTHOR
Mojtaba
Henteh
mhenteh@semnan.ac.ir
true
2
Ph.D. Candidate, Structure Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. Candidate, Structure Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. Candidate, Structure Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
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