An evolutionary computational method to formulate the response of unbonded concrete overlays to temperature loading

Abstract

PurposeUnbonded concrete overlays (UBOLs) are commonly used in pavement rehabilitation. The current models included in the Mechanistic-Empirical Pavement Design Guide cannot properly predict the structural response of UBOLs. In this paper, a multigene genetic programming (MGGP) approach is proposed to derive new prediction models for the UBOLs response to temperature loading.Design/methodology/approachMGGP is a promising variant of evolutionary computation capable of developing highly nonlinear explicit models for characterizing complex engineering problems. The proposed UBOL response models are formulated in terms of several influencing parameters including joint spacing, radius of relative stiffness, temperature gradient and adjusted load/pavement weight ratio. Furthermore, linear regression models are developed to benchmark the MGGP models.FindingsThe derived design equations accurately characterize the UBOLs response under temperature loading and remarkably outperform the regression models. The conducted parametric analysis implies the efficiency of the MGGP-based model in capturing the underlying physical behavior of the UBOLs response to temperature loading. Based on the results, the proposed models can be reliably deployed for design purposes.Originality/valueA challenge in the design of UBOLs is that their interlayer effects have not been directly considered in previous design procedures. To achieve better performance predictions, it is necessary to capture the effect of the interlayer in the design process. This study addresses this important issue via developing new models that can efficiently account for the effects of interlayer on the stress and deflections. In addition, it provides an insight into the effect of several parameters influencing the deflections of the UBOLs. From a computing perspective, a powerful evolutionary computation technique is introduced that overcomes the shortcomings of existing machine learning methods.

Prediction of effective equivalent linear temperature gradients in bonded concrete overlays of asphalt pavements

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“…Therefore, a range of critical hyperparameters were considered, and a total of 324 MGGP models were trained based on a full factorial design of the considered parameters. The range of each parameter was selected based on previous studies and are presented in Table 5 (Koza, 1992; Gandomi and Alavi, 2012; Zhang et al ., 2021; Ory et al ., 2010). A random 70/20/10 training/testing/validation split was applied to the original dataset of 24,336 EELTG values.…”

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

“…Details of training parameters and ranges used in this study are listed in Table 4. Parameter ranges were selected based on a trial study, and according to previous studies (Roy et al 2010;Gandomi and Alavi 2012;Zhang et al 2021). Three replications were conducted for every factor combination, and the GP algorithm was until no significant improvements was observed inLrn eff .…”