The optimization of energy consumption during asphalt mixture production and compaction is a challenge in producing durable, sustainable, and environmentally friendly asphalt products. This study investigated the effects of crude palm oil (CPO) and/or tire pyrolysis oil (TPO) on shear viscosity and mixing and compaction temperatures of asphalt. Moreover, the possibility of using response surface methodology (RSM) and machine learning (ML) to develop predictive models for the shear viscosity and mixing and compaction temperatures of CPO- and/or TPO-modified asphalt was studied and compared. The results showed that the mixing and compaction temperatures significantly decreased with increasing CPO and TPO, and the shear viscosity consequently declined because of the light components, resulting in softer binders. However, at 5% of both materials, a balance between the required temperatures and a similar or better viscosity compared to the base asphalt were demonstrated. RSM analysis showed that CPO had a significant effect on the viscosity and production temperatures of the base and modified asphalts compared with TPO, which had no significant effects. The developed predictive models based on RSM exhibited a correlation coefficient (R²) of more than 0.82 for all responses. In addition, it was found that extreme gradient boosting (XGB) regression was the best among all evaluated algorithms for predicting shear viscosity, whereas random forest regression (RFR) was the best for mixing and compaction temperatures, with R² values greater than 0.93. The performance evaluations of XGB and RFR showed extremely small differences between the predicted and experimental data. ML outperformed RSM in terms of prediction accuracy.

Pipelines are vital for transporting oil and gas, but leaks can have serious consequences such as fires, injuries, pollution, and property damage. Therefore, preserving pipeline integrity is crucial for a safe and sustainable energy supply. The rapid progress of machine learning (ML) technologies provides an advantageous opportunity to develop predictive models that can effectively tackle these challenges. This review article mainly focuses on the novelty of using machine and deep learning techniques, specifically artificial neural networks (ANNs), support vector machines (SVMs) and hybrid machine learning (HML) algorithms, for predicting different pipeline failures in the oil and gas industry. In contrast to existing noncomprehensive reviews on pipeline defects, this article explicitly addresses the application of ML techniques, parameters, and data reliability for this purpose. The article surveys research in this specific area, offering a coherent discussion and identifying the motivations and challenges associated with using ML for predicting different types of defects in pipelines. This review also includes a bibliometric analysis of the literature, highlighting common ML techniques, investigated failures, and experimental tests. It also provides in-depth details, summarized in tables, on different failure types, commonly used ML algorithms, and data resources, with critical discussions. Based on a comprehensive review aforementioned, it was found that ML approaches, specifically ANNs and SVMs, can accurately predict oil and gas pipeline failures compared to conventional methods. However, it is highly recommended to combine multiple ML algorithms to enhance accuracy and prediction time further. Comparing ML predictive models based on field, experimental, and simulation data for various pipeline failures can establish reliable and cost-effective monitoring systems for the entire pipeline network. This systematic review is expected to aid in understanding the existing research gaps and provide options for other researchers interested in predicting oil and gas pipeline failures.