L-shaped reinforced concrete retaining wall design: cost and sizing optimization

Abstract

In the context of this study, the design of L-shaped reinforced concrete retaining walls have been scrutinized parametrically depending on the simultaneous analysis of cost and sizing with the use of a recent optimization algorithm. The differences and restrictions of L-shaped reinforced concrete retaining wall design than classical T-shaped walls have been also discussed. The foundation width and the thickness of the wall required for a safe design has been also investigated according to the change of excavation depth, the type of soil dominating field and the external loading conditions. The observed results from optimization analyses shows that the variation of the shear strength angle is the most significant soil geotechnical parameter for supplying an envisaged safe design against sliding, overturning and adequate bearing capacity. Concurrently, the excavation depth is the most important factor that is forming the necessity of the construction of the retaining structure and optimal dimension evaluation. It is also proved that the wall foundation width is the most effected dimension of the retaining structures by the change of design parameters and the cost difference is directly influenced by the change of sizing. A cost-effective wall design can be performed with the use of proposed optimization analysis is capable in a shorter time than the traditional methods. Eventually, it has shown that such optimization methods may be useful to find the optimal design requirements for geotechnical engineering structures.

A generative design-to-BIM workflow for minimum weight plane truss design

“…For example, several metaheuristic algorithms have been developed for the design of optimal columns and beams with different objectives (minimum weight, cost). For example, the genetic algorithm (GA) (Coello, Hernández, & Farrera, 1997) and (T. Yousif & Najem, 2014); harmony search (HS) (Nigdeli et al, 2015); differential evolution method (DE) (Hieu & Tuan, 2018); particle swarm optimization (PSO) (Khashi, Dehghani, & Jahanara, 2018); artificial bee swarm (ABC), bat algorithm (BA), modified bat algorithm (MBA) (Yücel, Bekdaş, & Nı̇gdelı̇, 2020) ; HS (Kaveh & Shakouri Mahmud Abadi, 2011); Enhanced Charge System Search (ECSS) algorithm (Talatahari & Sheikholeslami, 2014); GA (Mohammad & Ahmed, 2018); Jaya algorithm (JA) (Kayabekir, Arama, Bekdaş, & Dalyan, 2020) were used to create an optimal designs. On the other hand, to create an effective vibration damping system with tuned mass dampers (TMD), base isolation or bracing systems in different structures HS and BA(Gebrail Bekdaş & Nigdeli, 2017); improved harmonic search (IHS) (Zhang & Zhang, 2017); center of gravity optimization (CMO) (Gholizadeh & Ebadijalal, 2018); flower pollination algorithm (FPA) (Yucel, Bekdaş, Nigdeli, & Sevgen, 2019); teaching and learning based optimization (TLBO) (Ulusoy, Niğdeli, & Bekdaş, 2019) were implemented.…”

Design Optimization of Reinforced Concrete Cantilever Retaining Walls: A State‐of‐the‐Art Review

“…ey demonstrated JA as an effective tool to apply optimization by a parametric analysis. Further studies which have successfully employed the JA are Yücel et al [33], Kayabekir et al [79], Eroglu et al [91], and Temür [99].…”

“…Optimization can be applied through multiple programming software; however, due to their complexities civil engineers often choose simpler programming languages such as MATLAB (Schmied and Karlsson [24], Pei and Xia [28], Al Sebai et al [31], Srivastavaa et al [32], Babu and Basha [36], Khajehzadeh et al [38], Khajehzadeh and Eslami [43], Sable and Patil [44], Sable and Patil [45], Kaveh and Behnam [46], Kaveh et al [47], Khajehzadeh et al [49], Gandomi et al [52], Kaveh and Mahdavi [53], Gandomi et al [61], Öztürk and Türkeli [70], Uray et al [71], Kalemci et al [78], Kayabekir [79], Kashani et al [82], Mevada et al [88], Uray et al [92], Tutus ¸et al [95], Khajehzadeh et al [101], and Khajehzadeh et al [103]), Fortran (Villalba et al [27] and Ukritchon et al [65]), C#.NET (Linh et al [93]), Python (Dodigović et al [94]), and C++ (Dagdeviren and Kaymak [72]). Studies have also tried to combine analysis software such as ABAQUS, ANSYS, PLAXIS 2D, and GeoSlope (Rahbari [23], Papazafeiropoulos et al [29], Uray and Tan [30], Rahbari et al [64], and Ravichandran et al [84]) with optimization.…”

The Usage of the Harmony Search Algorithm for the Optimal Design Problem of Reinforced Concrete Retaining Walls

“…Methods for obtaining cost and sizing equilibrium of reinforced concrete retaining walls have been the main objective of many studies over the years and continue to be relevant due to the developments in information and computer technologies. Metaheuristic algorithms such as the genetic algorithm (GA) [12,[26][27][28], big bang big crunch (BBBC) [29,30], particle swarm optimization (PSO) [31,32], firefly algorithm (FA) [33,34], harmony search (HS) algorithm [35,36], ant colony optimization (ACO), bat algorithm (BA) [37], charged system search (CSS) [38], simulated annealing (SA) [39][40][41], biogeography-based optimization algorithm (BBO) [42], teaching learning-based optimization (TLBO) [43], jaya algorithm (JA) [44], and flower pollination algorithm (FPA) [45] can be counted as the applied mew methods for the optimization process of retaining wall systems.…”