An Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm

Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.

[1]  Dong Wang,et al.  Smart bacteria‐foraging algorithm‐based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth‐rock dam , 2018, Expert Syst. J. Knowl. Eng..

[2]  Ioan-Daniel Borlea,et al.  Model-Free Sliding Mode and Fuzzy Controllers for Reverse Osmosis Desalination Plants , 2018 .

[3]  Simaan M. AbouRizk,et al.  Site Layout and Construction Plan Optimization Using an Integrated Genetic Algorithm Simulation Framework , 2017, J. Comput. Civ. Eng..

[4]  Payman Moallem,et al.  Training Echo State Neural Network Using Harmony Search Algorithm , 2017 .

[5]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[6]  Edmundas Kazimieras Zavadskas,et al.  State of art surveys of overviews on MCDM/MADM methods , 2014 .

[7]  Hong Zhang,et al.  Fuzzy-multi-objective particle swarm optimization for time―cost―quality tradeoff in construction , 2010 .

[8]  Srikrishna Subramanian,et al.  Grey wolf optimization for combined heat and power dispatch with cogeneration systems , 2016 .

[9]  V. Venkrbec,et al.  Construction process optimisation – review of methods, tools and applications , 2018, Journal of the Croatian Association of Civil Engineers.

[10]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[11]  Radu-Codrut David,et al.  Second Order Intelligent Proportional-Integral Fuzzy Control of Twin Rotor Aerodynamic Systems , 2018, ITQM.

[12]  Jun Zhang,et al.  Earth Dam Construction Simulation Considering Stochastic Rainfall Impact , 2018, Comput. Aided Civ. Infrastructure Eng..

[13]  Aviad Shapira,et al.  AHP-Based Equipment Selection Model for Construction Projects , 2005 .

[14]  Jun Zhang,et al.  Dynamic time-cost-quality tradeoff of rockfill dam construction based on real-time monitoring , 2017 .

[15]  Pan Feife.  Research on staged filling section optimization of high concrete-faced rock-fill dams , 2014 .

[16]  Sung-Lin Hsueh,et al.  A DFuzzy-DAHP Decision-Making Model for Evaluating Energy-Saving Design Strategies for Residential Buildings , 2012 .

[17]  Li Wang,et al.  A decision support system for substage-zoning filling design of rock-fill dams based on particle swarm optimization , 2011, Inf. Technol. Manag..

[18]  Diego Oliva,et al.  Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm , 2017 .

[19]  Tarek Hegazy,et al.  Resource Optimization Using Combined Simulation and Genetic Algorithms , 2003 .

[20]  Pan Yue,et al.  A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers , 2016, Expert Syst. Appl..

[21]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[22]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[23]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[24]  Xinyang Deng,et al.  Supplier selection using AHP methodology extended by D numbers , 2014, Expert Syst. Appl..

[25]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[26]  Yong Deng.  D Numbers: Theory and Applications ? , 2012 .

[27]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[28]  Yongquan Zhou,et al.  Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems , 2016 .

[29]  Mohd Wazir Mustafa,et al.  Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm , 2018, Energies.

[30]  Akhtar Kalam,et al.  Triple Bottom Line Analysis and Optimum Sizing of Renewable Energy Using Improved Hybrid Optimization Employing the Genetic Algorithm: A Case Study from India , 2019, Energies.

[31]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[32]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[33]  D. Zhong,et al.  Theory and practice of construction simulation for high rockfill dam , 2007 .

[34]  Thomas L. Saaty,et al.  The Modern Science of Multicriteria Decision Making and Its Practical Applications: The AHP/ANP Approach , 2013, Oper. Res..

[35]  Edmundas Kazimieras Zavadskas,et al.  A Hybrid MCDM Technique for Risk Management in Construction Projects , 2018, Symmetry.

[36]  Mujahed Al-Dhaifallah,et al.  A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids , 2018, Energies.

[37]  Saeid Nahavandi,et al.  Improving the Quality of Prediction Intervals Through Optimal Aggregation , 2015, IEEE Transactions on Industrial Electronics.

[38]  Haoran Zhao,et al.  Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm , 2017 .

[39]  Lifang Wang,et al.  Evaluation of university scientific research ability based on the output of sci-tech papers: A D-AHP approach , 2017, PloS one.

[40]  Radu-Emil Precup,et al.  An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning , 2017, Algorithms.

[41]  Himani Joshi,et al.  Enhanced Grey Wolf Optimization Algorithm for Global Optimization , 2017, Fundam. Informaticae.

[42]  Nelson F. F. Ebecken,et al.  Optimization of mass concrete construction using genetic algorithms , 2004 .

[43]  Mario Galić,et al.  Optimizacija građevinskih procesa – metode, alati i primjena , 2018 .

[44]  Konstantinos G. Margaritis,et al.  On benchmarking functions for genetic algorithms , 2001, Int. J. Comput. Math..

[45]  Yongquan Zhou,et al.  Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization , 2017, IEEE Access.

[46]  J. A. Charles,et al.  The engineering behaviour of fill materials: the use, misuse and disuse of case histories , 2008 .

[47]  Sriparna Saha,et al.  New cuckoo search algorithms with enhanced exploration and exploitation properties , 2018, Expert Syst. Appl..

[48]  Hui Gao,et al.  Particle swarm optimization-based machine arrangement for filling construction of rock-fill dams , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[49]  T. K. Sunil Kumar,et al.  Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms , 2018 .

[50]  E. Zavadskas,et al.  Equipment Selection Using Fuzzy Multi Criteria Decision Making Model: Key Study of Gole Gohar Iron Min , 2012 .

[51]  Li Chang,et al.  A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect , 2018, Energies.

[52]  Qidi Wu,et al.  A survey of biogeography-based optimization , 2017, Neural Computing and Applications.

[53]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[54]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[55]  Bo Cui,et al.  Theoretical research on construction quality real-time monitoring and system integration of core rockfill dam , 2009 .

[56]  Yong Deng,et al.  D-CFPR: D numbers extended consistent fuzzy preference relations , 2014, Knowl. Based Syst..

[57]  Natasa Prascevic,et al.  Application of fuzzy AHP for ranking and selection of alternatives in construction project management , 2017 .

[58]  Leonas Ustinovichius,et al.  Computer-aided decision-making in construction project development , 2015 .

[59]  Jia Yu,et al.  Construction Simulation for a Core Rockfill Dam Based on Optimal Construction Stages and Zones: Case Study , 2016, J. Comput. Civ. Eng..

[60]  G. Wiselin Jiji,et al.  An enhanced particle swarm optimization with levy flight for global optimization , 2016, Appl. Soft Comput..

[61]  R. A. Swief,et al.  A Reconfigured Whale Optimization Technique (RWOT) for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt , 2018 .