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Zeiada, Waleed

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Author-ID : 7996009

Civil Engineering, Building, Construction & Architecture

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Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles Abuzwidah, Muamer; Elawady, Ahmed; Wang, Ling; Zeiada, Waleed
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-019

Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility. Doi: 10.28991/CEJ-2024-010-09-019 Full Text: PDF

Benchmarking Classical and Deep Machine Learning Models for Predicting Hot Mix Asphalt Dynamic Modulus Zeiada, Waleed; Obaid, Lubna; El-Badawy, Sherif; Abd El-Hakim, Ragaa; Awed, Ahmed
Civil Engineering Journal Vol 11, No 1 (2025): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-01-06

The dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures’ aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. Doi: 10.28991/CEJ-2025-011-01-06 Full Text: PDF

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Journal : Civil Engineering Journal