Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Abstract

Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

“…The results were that the GWO algorithm with a higher coefficient of determination achieved higher efficiency. Furthermore, Abdelkader et al [35] generated an integrated deterioration prediction model, which was envisioned via two fundamental components. The first component presented an integrated Gaussian process regression model and a grey wolf optimization algorithm.…”

Asset Allocation through Grey Wolf Optimization: A Case of KSE-30 Index

“…Hasterok et al (2021) present an optimization model based on the Grey Wolf Optimizer meta-heuristic to enable the definition of ideal energy mix considering the investment and operating expenses, taking into account the power and heating demand projection. Cast-inplace tunnel liners, concrete inner walls, a concrete portal, a concrete ceiling slab, and a concrete slab on grade are the five parts of a highway tunnel that (Abdelkader, Al-Sakkaf, Elshaboury, & Alfalah, 2022) suggest an integrated deterioration prediction model for. The built deterioration model is thought to include two main parts: calibration and evaluation.…”

Study on the Mechanical Response Mechanism and Damage Behavior of a Tunnel Lining Structure under Reverse Fault Dislocation

“…They are vulnerable to higher deterioration limits, but the funds available for their maintenance and rehabilitation are limited. This situation requires the development of a degradation model to predict the performance state behavior of key tunnel components [3]. The influence of cyclic load, cumulative load, and other loads on buildings is complex and diverse [4,5].…”