A regression-based approach for estimating preliminary dimensioning of reinforced concrete cantilever retaining walls

  • Ugur DagdevirenEmail author
  • Burak Kaymak
Research Paper


The reinforced concrete cantilever retaining walls (RCCRWs) are among the most commonly used type of structures to support the soil in civil engineering applications. In the conventional trial and error design of RCCRWs, which are based on engineering experiences and literature reviews, the preliminary dimensions of the wall are selected by considering the wall height only. However, it is known that the properties of backfill soil and surcharge loads also affect the dimensions of the wall. Therefore, in order to take into account the effects of the backfill soil properties and surcharge loads in addition to the height of the wall, a new regression-based approach is developed for predicting the preliminary dimensions of T-shaped RCCRWs. For this aim, a total of 375 optimization analyses are carried out for the optimum design of RCCRWs resting on soil with high bearing capacity by using the artificial bee colony (ABC) algorithm. Based on these calculated optimum solutions, the regression equations are developed for preliminary dimensioning of the T-shaped RCCRWs by using multiple regression analyses. Moreover, a set of 15 random problems are generated to assess prediction ability of the proposed regression equations, and their optimum dimensions are calculated by ABC algorithm and then these calculated dimensions are compared with the preliminary dimensions estimated by the proposed regression equations. From this comparison, it is observed that the maximum difference between the calculated and the estimated wall dimensions is only 6.2%. This means that the proposed preliminary dimensioning regression equations are capable of predicting dimensions that are close enough to the optimum dimensions. Therefore, for the most economical design of the T-shaped RCCRWs resting on soil with high bearing capacity, the predicted dimensions, which are supplied by the proposed regression equations, can be used as a good starting point when an optimization technique or a conventional trial and error method is employed.


Artificial bee colony (ABC) Multiple regression model Optimization Preliminary dimensioning Retaining walls 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Replication of results

The optimization algorithm and the design details of RCCRWs used the study are given in Sections 23, and 4. The data set used in the developed regression models for estimation of preliminary dimensions of the RCCRWs is obtained from a total of 375 optimization problem results using the artificial bee colony algorithm, and the data set is given in the Supplementary Table 1. Also, 15 different data set used to test and verify the proposed regression models are given in the Supplementary Table 2.

Supplementary material

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Civil EngineeringKutahya Dumlupinar UniversityKutahyaTurkey

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