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Unbonded 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.

MGGP 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.

The 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.

A 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.

The time-varying equivalent linear temperature gradient (ELTG) significantly affects the development of faulting and must therefore be accounted for in pavement design. The same is true for faulting of bonded concrete overlays of asphalt (BCOA) with slabs larger than 3 x 3 m. However, the evaluation of ELTG in Mechanistic-Empirical (ME) BCOA design is highly time-consuming. The use of an effective ELTG (EELTG) is an efficient alternative to calculating ELTG. In this study, a model to quickly evaluate EELTG was developed for faulting in BCOA for panels 3 m or longer in size, whose faulting is sensitive to ELTG.

A database of EELTG responses was generated for 144 BCOAs at 169 locations throughout the continental United States, which was used to develop a series of prediction models. Three methods were evaluated: multiple linear regression (MLR), artificial neural networks (ANNs), and multi-gene genetic programming (MGGP). The performance of each method was compared, considering both accuracy and model complexity.

It was shown that ANNs display the highest accuracy, with an R² of 0.90 on the validation dataset. MLR and MGGP models achieved R² of 0.73 and 0.71, respectively. However, these models consisted of far fewer free parameters as compared to the ANNs. The model comparison performed in this study highlights the need for researchers to consider the complexity of models so that their direct implementation is feasible.

This research produced a rapid EELTG prediction model for BCOAs that can be incorporated into the existing faulting model framework.

Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs.

Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement.

The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load.

The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.