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Driving in foggy conditions poses high risks to road users due to the reduction of visibility, affecting the drivers’ vision and perception, and making changes in driving behavior, which is one of the most important factors affecting vehicular emissions and fuel consumption. This study analyzes the PTV VISSIM traffic microsimulation outputs for exhaust emissions and fuel consumption of vehicles simulated under adverse weather conditions. This weather-dependent simulation is developed by using the advanced psychophysical car-following model “Wiedemann’s 99,” to flexibly control the driving behavior parameters in various driving conditions. Results show that vehicles under foggy conditions consume more fuel and produce more emissions in comparison with clear sky conditions and other scenarios. With the transition of current cities to smart sustainable cities and by introducing automated vehicles (AVs) to the traditional traffic network and gradually increasing their penetration rate, negative environmental impacts of driving under foggy conditions will be reduced, and improvement in overall mobility of a shared network of autonomous and human-driven is observable. 

Each year, the global cost that is accounted to corrosion was estimated at $2.5 trillion. Corrosion not only imposes an economic burden, when corroded structures are under various loading conditions, it may also lead to structurally brittle failure, posing a potential threat to structural reliability and service safety. Although considerable studies investigated the combined effect of external loads and structural steel corrosion, many of the current findings on synergetic interaction between stress and corrosion are contrary. In this study, the combined effects of dynamic mechanical loads and corrosion on epoxy coated steel are investigated using the distributed fiber optic sensors based on optical frequency domain reflectometry. Experimental studies were performed using the serpentine-arranged distributed fiber optic strain sensors embedded inside the epoxy with three different scenarios including the impact loading-only, corrosion-only, and combined impact loading-corrosion tests. Test results demonstrated that the distributed fiber optic sensors can locate and detect the corrosion processing paths by measuring the induced strain changes. The combined impact loading-corrosion condition showed significantly accelerated corrosion progression caused by mechanical loads, indicating the significant interaction between dynamic mechanical loading and corrosion on epoxy coated steel. 

This paper examines the impact of fire on the microstructural, mechanical, and corrosion behavior of wire-arc-sprayed zinc, aluminum, and Zn-Al pseudo-alloy coatings. Steel plates coated with these materials were subjected to temperatures in increments of 100 °C, starting from 300 °C and progressing until failure. Microstructural characterization, microhardness, abrasion resistance, and electrochemical impedance studies were performed on the post-fire coatings. The findings from this study show that heat had a positive impact on the performance of zinc and Zn-Al pseudo-alloy coatings when they were exposed to temperatures of up to 400 °C, while aluminum coatings maintain their performance up to 600 °C. However, above these temperatures, the effectiveness of coatings was observed to decline, due to increased high-temperature oxidation, and porosity, in addition to decreased microhardness, abrasion resistance, and corrosion protection performance. Based on the findings from this study, appropriately sealed thermal-spray-coated steel components can be reused after exposure to fire up to a specific temperature depending on the coating material. 

Pipeline networks are a crucial component of energy infrastructure, and natural force damage is an inevitable and unpredictable cause of pipeline failures. Such incidents can result in catastrophic losses, including harm to operators, communities, and the environment. Understanding the causes and impact of these failures is critical to preventing future incidents. This study investigates artificial intelligence (AI) algorithms to predict natural gas pipeline failures caused by natural forces, using climate change data that are incorporated into pipeline incident data. The AI algorithms were applied to the publicly available Pipeline and Hazardous Material Safety Administration (PHMSA) dataset from 2010 to 2022 for predicting future patterns. After data pre-processing and feature selection, the proposed model achieved a high prediction accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The AI models can help identify high-risk pipelines and prioritize inspection and maintenance activities, leading to cost savings and improved safety. The predictive capabilities of the models can be leveraged by transportation agencies responsible for pipeline management to prevent pipeline damage, reduce environmental damage, and effectively allocate resources. This study highlights the potential of machine learning techniques in predicting pipeline damage caused by natural forces and underscores the need for further research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance. 

The oil and gas (O&G) sector is a critical energy infrastructure to a Nation’s welfare. As developed as the O&G industry may seem, its aging infrastructure gradually shows numerous challenges to keep up with the growing energy demand, increasing operation costs, and environmental concerns. A robust O&G infrastructure that is risk-free, reliable, and resilient towards expected or unexpected threats can offer an uninterrupted supply of O&G to downstream stakeholders, competitive prices to customers, and better environmental footprints. With the shift towards renewable energy, the notion of sustainable development should be firmly embedded in O&G infrastructure and operations to facilitate the smooth transition towards future renewable energy generation. This paper offers a comprehensive and innovative approach to achieving sustainable development for O&G infrastructure by examining it from a holistic risk, reliability, and resilience (3Rs) perspective. The role of each individual concept and their collective influence on sustainable development in the O&G industry will be thoroughly discussed. Moreover, this paper will highlight the significant impact of the holistic 3Rs approach on sustainable development and propose future research directions. Given the complexity of O&G infrastructure, it is crucial to incorporate sustainable development practices into every dimension of the O&G infrastructure, iteratively and continuously, to achieve the ultimate goal of long-term sustainability. This paper makes a significant contribution to the field by providing valuable insights and recommendations for achieving sustainable development in the O&G industry. 

Modeling corrosion growth for complex systems such as the oil refinery system is a major challenge since the corrosion process of oil and gas pipelines are inherently stochastic and depends on many factors including exposures to environmental conditions, operating conditions, and electrochemical reactions. Moreover, the number of sensors is usually limited, and sensor data are incomplete and scattering, which hinders the capability of capturing the corrosion growth behaviors. Therefore, this paper proposes Multi-sensor Corrosion Growth Model with Latent Variables to predict the corrosion growth process in oil refinery piping. The proposed model is a combination of the hierarchical clustering algorithm and the vector autoregression (VAR) model. The clustering algorithm aims to find the hidden (i.e., latent) data clusters of the measured time series data, from which the time series from the same cluster will be included in the VAR model to predict the corrosion depth from multiple sensors. The model can capture the relationship between sensor time series data and identify latent variables. A real case study of an oil refinery system, in which in-line inspection (ILI) data were collected, was utilized to validate model. Regarding corrosion growth prediction, the paper compared the prediction accuracy of VAR model with other three forms of power law model, which is widely accepted to expect the time-dependent depth of corrosion such as power function (PF), PF with initiation time of corrosion (PFIT), and PF with initiation time of corrosion and covariates (PFCOV). The results showed that VAR model has the lowest prediction error based on the mean absolute percentage error (MAPE) evaluation for test data. Finally, the proposed model is believed to be useful for dealing with a complex system that has a variety of corrosion growth behaviors, such as the oil refinery system, as well as it can be applied in other real-time applications. 

One essential task in practice is to quantify and improve the reliability of an infrastructure network in terms of the connectivity of network components (i.e., all-terminal reliability). However, as the number of edges and nodes in the network increases, computing the all-terminal network reliability using exact algorithms becomes prohibitive. This is extremely burdensome in network designs requiring repeated computations. In this paper, we propose a novel machine learning-based framework for evaluating and improving all-terminal network reliability using Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL). With the help of DNNs and Stochastic Variational Inference (SVI), we can effectively compute the all-terminal reliability for different network configurations in DRL. Furthermore, the Bayesian nature of the proposed SVI+DNN model allows for quantifying the estimation uncertainty while enforcing regularization and reducing overfitting. Our numerical experiment and case study show that the proposed framework provides an effective tool for infrastructure network reliability improvement.