A parametric study on the cost optimization of a reinforced concrete abutment using a genetic algorithm

Abstract

This paper presents the cost optimization of a reinforced concrete abutment of a cantilever type using a genetic algorithm (GA). During the optimization process, six design variables, including two geometrical design variables and four cross-sectional design variables, were considered. The objective function consists of the cost of steel, concrete, and labor. Computation programs have been developed in JAVA to find an economical design adhering to Indian Road Congress (IRC) standards. To get an optimal solution in reasonable computational time, an attempt is carried out to evaluate the optimal GA parameters for the abutment model. A parametric study was conducted to understand the effect of the angle of friction, grade of concrete, and height on the cost optimization of the abutment. From the parametric study, it is observed that optimum cost of the abutment is obtained with a higher value of the angle of friction and concrete with a lower compressive strength. The results of the optimization are further discussed in detail.

Impacts of Local Socio-Economic-Political Constraints on the Selection of Bridge Type, Form, and Aesthetics

“…For instance, most of the bridge flyovers in small urban cities and rural areas in India on highways employ reinforced concrete bridges (Kumari et al, 2022). These projects may have never considered the use of prestressed girders, stone arch bridges or steel arch bridges or composite steel-concrete bridges which are neither special nor novel.…”

Optimization of Glulam Regular Double-Tapered Beams for Agroforestry Constructions

“…The slope of the plane is significantly different from 0, since the values of the t-test for Snow, Span, and the combination of Snow and Span are −12.4 (p < 0.001), 98.2 (p < 0.001), 48.8 (p < 0.001), respectively. From the coefficients of the multiple regression shown in Table 2, the Equation ( 20) is proposed to predict the cost of the timber roof structure: Cost =(13.837 − 1.431 × Snow + 0.656 × Span + 0.245 × Snow × Span) 3.125 , (20) where: Cost is the structure investment (EUR), Snow is the applicable snow load (kN/m 2 ) and Span is the full dimension of the regular double-tapered beam (m). Figure 4 displays the resulting plane of the multiple regression for the Snow, Span, and Cost variables.…”

“…Although research on structural optimization dates back to the 1970s [5][6][7], only in the last two decades has there been significant progress in its development [8][9][10][11][12], largely due to the implementation of artificial intelligence techniques such as genetic algorithms. However, most of the research focuses on steel [13][14][15][16] and concrete [17][18][19][20] structures, with timber structures [21][22][23] largely ignored by the scientific community.…”