Sajib Saha

Rasel Hossen, Diptajoy Mistry, Mushiur Rahman et al.

Urban safety and infrastructure maintenance are critical components of smart city development. Manual monitoring of road damages is time-consuming, highly costly, and error-prone. This paper presents a deep learning approach for automated road damage and manhole detection using the YOLOv9 algorithm with polygonal annotations. Unlike traditional bounding box annotation, we employ polygonal annotations for more precise localization of road defects. We develop a novel dataset comprising more than one thousand images which are mostly collected from Dhaka, Bangladesh. This dataset is used to train a YOLO-based model for three classes, namely Broken, Not Broken, and Manhole. We achieve 78.1% overall image-level accuracy. The YOLOv9 model demonstrates strong performance for Broken (86.7% F1-score) and Not Broken (89.2% F1-score) classes, with challenges in Manhole detection (18.2% F1-score) due to class imbalance. Our approach offers an efficient and scalable solution for monitoring urban infrastructure in developing countries.

Sajib Saha, Fan Gu, Xue Luo et al.

The performance of rigid pavement is greatly affected by the properties of base/subbase as well as subgrade layer. However, the performance predicted by the AASHTOWare Pavement ME design shows low sensitivity to the properties of base and subgrade layers. To improve the sensitivity and better reflect the influence of unbound layers a new set of improved models i.e., resilient modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this study. An Artificial Neural Network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation datasets in the ANN model consist of 27000 simulation cases with different combinations of pavement layer thickness, layer modulus and slab-base interface bond ratio. To examine the sensitivity of modified MR and k-values on pavement response, eight pavement sections data are collected from the Long-Term Pavement performance (LTPP) database and modeled by using the FE software ISLAB2000. The computational results indicate that the modified MR values have higher sensitivity to water content in base layer on critical stress and deflection response of rigid pavements compared to the results using the Pavement ME design model. It is also observed that the k-values using ANN model has the capability of predicting critical pavement response at any partially bonded conditions whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonding and no bonding).