TY - JOUR ID - 107850 TI - Shape Optimization of Gravity Dams Using a Nature-Inspired Approach JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Ferdowsi, Ahmad AU - Hoseini, Seyed Mohamad AU - Farzin, Saeed AU - Faramarzpour, Mahtab AU - Mousavi, Sayed Farhad AD - M.Sc. Graduate, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran AD - M.Sc. Student, Department of Civil Engineering, University of Science and Culture, Tehran, Iran AD - Assistant Professor, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran AD - Lecturer, Department of Civil Engineering, Robat Karim Branch, Islamic Azad University, Tehran, Iran AD - Professor, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran Y1 - 2020 PY - 2020 VL - 4 IS - 3 SP - 65 EP - 78 KW - Concrete Gravity Dams KW - Optimum Design KW - Nature-Inspired Algorithms KW - Invasive Weed Optimization (IWO) Algorithm KW - Shape Optimization DO - 10.22115/scce.2020.224492.1196 N2 - In water infrastructures design problems, small changes in their geometries lead to a major variation in the construction time and costs. Dams are such important water infrastructures, which have different types regarding their materials and their behavior to endure loads. In the current paper, invasive weed optimization (IWO) algorithm is employed to find the best shape of a concrete gravity dam (Tilari Dam, India). Stress and stability were considered as design constraints, based on the following models: Model I (M1): upstream dam face is inclined and Model II (M2): upstream dam face is vertical. Optimization using IWO for M1 showed 20% reduction in cross-sectional area as compared to prototype. Although results obtained using IWO showed no changes in comparison with the algorithms in the literature (i.e., differential evolution, charged system search, colliding bodies optimization, and enhanced colliding bodies optimization), it converged faster. But results for M2 revealed 26% reduction in cross-sectional area. UR - https://www.jsoftcivil.com/article_107850.html L1 - https://www.jsoftcivil.com/article_107850_33353ae971388e14e7c3d66868c4ac1f.pdf ER -