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.
Highlights
Infrastructure systems in the United States are prone to large levels of deterioration and require investment
It is noted that the reported deterioration models did not look into the performance condition of some important tunnel elements like interior walls, portals, ceiling slabs, and slabs on grade despite them having a significant impact on the integrity of the entire highway tunnel system
The optimum solution found by the grey wolf optimization algorithm is appended to be further used in validating the developed GWO-GPR model for deterioration prediction of highway tunnel elements
Summary
Infrastructure systems in the United States are prone to large levels of deterioration and require investment. The criticality of highway tunnels coupled with their old age and the increase in traffic volumes necessitate efficient monitoring of the structural condition of highway tunnels over time to ensure their timely maintenance, repair, and rehabilitation. Artificial intelligence techniques have been successfully implemented in maintenance management [4,5]. These techniques are of high data-demanding character due to the significant data needed in building, learning, and validating a reliable prediction model [6,7]. Develop a hybrid Gaussian process regression–grey wolf optimization model for simulating the deterioration process of a set of highway tunnel components. Validate the developed deterioration model against a set of widely used machine learning models using performance evaluation and statistical comparisons
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