Optimum Design of Reinforced Concrete Cantilever Retaining Walls according Eurocode 2 (EC2)

Abstract

This study investigates optimum design in terms of minimum cost of reinforced concrete cantilever retaining walls. For the optimization process, the evolutionary method which is a combination of genetic algorithm and local search techniques was implemented. Evolutionary method was adopted in this study because it can effectively solve highly nonlinear problems and problems that feature discontinuous functions as demonstrated by several works available in the literature. The popularity of the evolutionary method may also be attributed to its availability as one of the solving methods in Solver add-in tool of Microsoft Excel. This implies that it is freely available and no need to pay for extra license to run any optimization problem. The design variables of the problem are thickness of stem wall, thickness of base slab, width of the heel, width of the toe, area of steel reinforcement for the stem wall and base slab. The objective function was to minimise the total cost of the wall, which includes costs of concrete, steel, forming, and excavation. The constrained functions were set to satisfy provisions and requirements of Eurocode 2 (EC2). Material strength and soil characteristics are treated as design parameters where they are kept constants during solution of the problem. Various material cost ratios were considered. Consequently, optimum design charts were developed for a wide range of wall height, coefficient of friction and surcharge load. Following a comprehensive investigation of the minimum cost problems carried out for different cases, one can conclude that the total cost of the retaining wall is directly proportional to the wall height and surcharge load values, whereas, the cost is almost independent of coefficient of friction.

Effects of Concrete Grades and Column Spacings on the Optimal Design of Reinforced Concrete Buildings

“…These algorithms include artificial bee colony algorithm [3], genetic algorithms [17][18][19][20], simulated annealing [14,21], ant colony optimization [22,23], harmony search [13,24,25], cuckoo search [26], and firefly algorithm [27]. Because the design office practice is mostly based on simple Microsoft Excel spreadsheets, several researchers tested the applicability of the solver tool provided by Microsoft Excel in the field of structural optimization [12,16,[28][29][30][31]. The optimization problems in these studies were limited to minimizing the cost of individual structural components such as slabs, beams, footings, and retaining walls.…”

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

“…e cost of concrete can be expanded to include the cost of formwork, transportation, labor, vibration, Earth removal, and cost of backfill as done by Naeem [22], Villalba et al [27], Al Sebai et al [31], Ceranic et al [34], Yepes et al [37], Camp and Akin [42], Sable and Patil [45], Kaveh et al [47], Talatahari and Sheikholeslami [51], Kaveh and Farhoudi [56], Temür and Bekdas [58], Mohammad and Ahmed [67], Moayyeri et al [69], Konstandakopoulou et al [76], Mergos and Mantoglou [77], Tousi et al [90], and Dodigović et al [94]. e cost of varying concrete and steel strength can also be used for optimization as done by Villalba et al [27], Yepes et al [37], Kaveh et al [47], Kalateh-Ahani and Sarani [68], Konstandakopoulou et al [76], Tousi et al [90], and Shakeel et al [100].…”

“…e cost of varying concrete and steel strength can also be used for optimization as done by Villalba et al [27], Yepes et al [37], Kaveh et al [47], Kalateh-Ahani and Sarani [68], Konstandakopoulou et al [76], Tousi et al [90], and Shakeel et al [100]. e research of Mohammad and Ahmed [67] has used cost ratios to simplify the results and lessen the effect of local currency on optimal results. e second most used objective is weight minimization.…”