A Bayesian decision support system for optimizing pavement management programs.

Over time, the pavement deteriorates due to traffic and the environment, resulting in poor riding quality and structural inadequacies. Evaluating pavement condition over time is thus a critical component of any pavement management system (PMS) to extend the service life of pavements. However, the uncertainty associated with the pavement deterioration process due to the heterogeneous nature of the pavement degradation factors makes the process difficult.

Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps.

The world has faced the COVID-19 pandemic for over two years now, and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements. The interlink between these measures and the resulting change in mobility (a predictor of the disease transmission contact rate) is uncertain. We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions (NPI) and examine the aptness of incorporating mobility data for epidemiological modelling.

Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities.

With the increasing global adoption of COVID-19 vaccines, limitations on mass gathering events have started to gradually loosen. However, the large vaccine inequality recorded among different countries is an important aspect that policymakers must address when implementing control measures for such events. In this paper, we propose a model for the assessment of different control measures with the consideration of vaccine inequality in the population.

Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures.

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.