Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm

Hanaa H. Lateef, Abbas Mohammed Burhan

Abstract


The cost of pile foundations is part of the super structure cost, and it became necessary to reduce this cost by studying the pile types then decision-making in the selection of the optimal pile type in terms of cost and time of production and quality .So The main objective of this study is to solve the time–cost–quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing" cost and time while "maximizing" quality. There are many types In the world of piles but  in this paper, the researcher proposed five pile types, one of them is not a traditional, and   developed a model for the problem and then employed particle swarm optimization (PSO) algorithm, as one of evolutionary algorithms with the help of (Mat lab software), as a tool for decision making problem about choosing the best alternative of the traded piles, and proposes a multi objective optimization model, which aims to optimize the time, cost and quality of the pile types, and assist in selecting the most appropriate pile types. The researcher selected 10 of senior engineers to conduct interviews with them.  And prepared some questions for interviews and open questionnaire. The individuals are selected from private and state sectors each one have 10 years or more experience in pile foundations work. From personal interviews and field survey the research has shown that most of the experts, engineers are not fully aware of new soft wear techniques to helps them in choosing alternatives, despite their belief in the usefulness of using modern technology and software. The Problem is multi objective optimization problem, so after running the PSO algorithm it is usual to have more than one optimal solution, for five proposed pile types, finally the researcher  evaluated and  discussed the output results and  found out that pre-high tension spun (PHC)pile type was the optimal pile type.


Keywords


PSO Algorithm; PHC; Optimal Pile Type; Decision Making.

References


Letsios, Christos, Nikos D. Lagaros, and Manolis Papadrakakis. “Optimum Design Methodologies for Pile Foundations in London.” Case Studies in Structural Engineering 2 (December 2014): 24–32. doi:10.1016/j.csse.2014.08.001.

Fellenius, Bengt H. “Pile Foundations.” Foundation Engineering Handbook (1991): 511–536. doi:10.1007/978-1-4757-5271-7_13.

Bartolomei, A. A. “Pile Foundation Engineering.” Soil Mechanics and Foundation Engineering 32, no. 3 (May 1995): 73–75. doi:10.1007/bf02336500.

Nagai, Hiroshi, Tsutomu Tsuchiya, and Masao Shimada. “Influence of Installation Method on Performance of Screwed Pile and Evaluation of Pulling Resistance.” Soils and Foundations 58, no. 2 (April 2018): 355–369. doi:10.1016/j.sandf.2018.02.006.

Zhang, Jian, and Yang Gan. “Optimization of Multi-Objective Micro-Grid Based on Improved Particle Swarm Optimization Algorithm” (2018). doi:10.1063/1.5033673.

Jeter, Melvyn W. “An Introduction to Mathematical Programming.” Mathematical Programming (May 3, 2018): 1–26. doi:10.1201/9780203749333-1.

Chiulli, Roy M. “Linear Programming (LP).” Quantitative Analysis (April 27, 2018): 311–375. doi:10.1201/9780203741559-9.

“A Flyover Introduction to Integer Linear Programming.” Integer Linear Programming in Computational and Systems Biology (June 13, 2019): 3–14. doi:10.1017/9781108377737.003.

“Adaptive Dynamic Programming for Uncertain Linear Systems.” Robust Adaptive Dynamic Programming (April 14, 2017): 11–33. doi:10.1002/9781119132677.ch2.

Mavrovouniotis, Michalis, Changhe Li, and Shengxiang Yang. “A Survey of Swarm Intelligence for Dynamic Optimization: Algorithms and Applications.” Swarm and Evolutionary Computation 33 (April 2017): 1–17. doi:10.1016/j.swevo.2016.12.005.

Miladi Rad, Kaveh, and Omid Aminoroayaie Yamini. “The Methodology of Using Value Engineering in Construction Projects Management.” Civil Engineering Journal 2, no. 6 (July 1, 2016): 262. doi:10.28991/cej-030986.

Drechsler, Rolf. “Heuristic Learning.” Evolutionary Algorithms for VLSI CAD (1998): 147–163. doi:10.1007/978-1-4757-2866-8_7.

Mavrovouniotis, Michalis, Changhe Li, and Shengxiang Yang. “A Survey of Swarm Intelligence for Dynamic Optimization: Algorithms and Applications.” Swarm and Evolutionary Computation 33 (April 2017): 1–17. doi:10.1016/j.swevo.2016.12.005.

Ertenlice, Okkes, and Can B. Kalayci. “A Survey of Swarm Intelligence for Portfolio Optimization: Algorithms and Applications.” Swarm and Evolutionary Computation 39 (April 2018): 36–52. doi:10.1016/j.swevo.2018.01.009.

Luo, Juanjuan, Huadong Ma, and Dongqing Zhou. “Multiobjective Memetic Algorithm for Vital Nodes Identification in Complex Networks.” 2019 IEEE Congress on Evolutionary Computation (CEC) (June 2019). doi:10.1109/cec.2019.8789969.

Kotinis, Miltiadis. “A Particle Swarm Optimizer for Constrained Multi-Objective Engineering Design Problems.” Engineering Optimization 42, no. 10 (October 2010): 907–926. doi:10.1080/03052150903505877.

Parsopoulos, Konstantinos E., and Michael N. Vrahatis. “Multi-Objective Particles Swarm Optimization Approaches.” Multi-Objective Optimization in Computational Intelligence (n.d.): 20–42. doi:10.4018/978-1-59904-498-9.ch002.

Sinan Hasanoglu, Mehmet, and Melik Dolen. “Multi-Objective Feasibility Enhanced Particle Swarm Optimization.” Engineering Optimization 50, no. 12 (February 27, 2018): 2013–2037. doi:10.1080/0305215x.2018.1431232.

Afshar, A., F. Sharifi, and M. R. Jalali. “Non-Dominated Archiving Multi-Colony Ant Algorithm for Multi-Objective Optimization: Application to Multi-Purpose Reservoir Operation.” Engineering Optimization 41, no. 4 (April 2009): 313–325. doi:10.1080/03052150802460414.

Mavrovouniotis, Michalis, and Shengxiang Yang. “Ant Colony Optimization for Dynamic Combinatorial Optimization Problems.” Swarm Intelligence - Volume 1: Principles, Current Algorithms and Methods (n.d.): 121–142. doi:10.1049/pbce119f_ch5.

“Shuffled Frog-Leaping Algorithm.” Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization (September 8, 2017): 133–143. doi:10.1002/9781119387053.ch11.

Tiwari, Santosh, Patrick Koch, Georges Fadel, and Kalyanmoy Deb. “AMGA.” Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation - GECCO ’08 (2008). doi:10.1145/1389095.1389235.

Jatana, Nishtha, and Bharti Suri. “Particle Swarm and Genetic Algorithm Applied to Mutation Testing for Test Data Generation: A Comparative Evaluation.” Journal of King Saud University - Computer and Information Sciences (May 2019). doi:10.1016/j.jksuci.2019.05.004.

Yang, Feng, Pengxiang Wang, Yizhai Zhang, Litao Zheng, and Jianchun Lu. “Survey of Swarm Intelligence Optimization Algorithms.” 2017 IEEE International Conference on Unmanned Systems (ICUS) (October 2017). doi:10.1109/icus.2017.8278405.

Alizadeh, M. J., A. Shabani, and M. R. Kavianpour. “Predicting Longitudinal Dispersion Coefficient Using ANN with Metaheuristic Training Algorithms.” International Journal of Environmental Science and Technology 14, no. 11 (March 13, 2017): 2399–2410. doi:10.1007/s13762-017-1307-1.

Phan, Han Duy, Kirsten Ellis, Jan Carlo Barca, and Alan Dorin. “A Survey of Dynamic Parameter Setting Methods for Nature-Inspired Swarm Intelligence Algorithms.” Neural Computing and Applications (May 20, 2019). doi:10.1007/s00521-019-04229-2.

Trivedi, Vibhu, Pushkar Varshney, and Manojkumar Ramteke. “A Simplified Multi-Objective Particle Swarm Optimization Algorithm.” Swarm Intelligence (July 15, 2019). doi:10.1007/s11721-019-00170-1.

Jonathan E. Fieldsend, “Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization”, Book Chapter published 2009 in Studies in Computational Intelligence on pages 93 to 114, https://doi.org/10.1007/978-3-642-03625-5_5.

Liang, Caihang, and Si Zeng. “Multi-Objective Design Optimization of Hollow Fiber Membrane-Based Liquid Desiccant Module Using Particle Swarm Optimization Algorithm.” Heat Transfer Engineering 39, no. 17–18 (October 5, 2017): 1605–1615. doi:10.1080/01457632.2017.1370316..

Xu, Ming, and JiangPing Gu. “Parameter Selection for Particle Swarm Optimization Based on Stochastic Multi-Objective Optimization.” 2015 Chinese Automation Congress (CAC) (November 2015). doi:10.1109/cac.2015.7382846.

He, Yan, Wei Jin Ma, and Ji Ping Zhang. “The Parameters Selection of PSO Algorithm Influencing On Performance of Fault Diagnosis.” Edited by J.C.M. Kao and W.-P. Sung. MATEC Web of Conferences 63 (2016): 02019. doi:10.1051/matecconf/20166302019.


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DOI: 10.28991/cej-2019-03091424

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