Aging and Rheological Properties of Latex and Crumb Rubber Modified Bitumen Using Dynamic Shear Rheometer

Rubberized bitumen technology has been applied for a long time to enhance the performance of the asphalt pavement. In this research, the influence of natural rubber (NR) latex and crumb rubber (CR) on the conventional and rheological characteristics of 80/100 penetration grade bitumen before and after aging was compared. Conventional tests of penetration, ring and ball temperature, and ductility were conducted to evaluate the consistency of base bitumen and rubberized bitumen. A dynamic shear rheometer (DSR) test was carried out to evaluate the viscoelastic characteristics of the base and rubberized bitumen. The results showed that the addition of NR latex and CR reduced the penetration grade and increased the ring and ball temperature and ductility of the rubberized bitumen. This indicates that promising enhancement of the bitumen properties can be expected with the addition of NR latex and CR. The rheological properties analysis results showed that addition of CR up to 8% and NR latex up to 4% improved the complex modulus and rutting resistance of the rubberized bitumen compared to conventional bitumen. This indicates that the application of NR latex and CR in bitumen modification is expected to improve the durability of asphalt pavement.

Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review

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

Smartphone applications for pavement condition monitoring: A review

Pavement condition monitoring (PCM) systems are essential for making decisions on road maintenance and rehabilitation toward preserving roads and airports assets in a good performance for a longer time. Modern smartphones are equipped with adequate storage, computing and communication properties, besides built-in sensors that show an excellent capability to capture information about users and the environment around us. Therefore, it is worthy to be used for efficient and cost-effective PCM. This review aims to survey the researchers' efforts on the application of smartphones for PCM, mapping the researchers' views from the literature into coherent discussions and highlighting the motivations and challenges of using such technology for pavement defects detection. Based on the existing literature, it was found that the smartphone applications technology is feasible and accurate to some extent as an alternative for conventional technologies for rural, highways and airports PCM. However, this technology is still in the first stage and many factors, calibrations and standards need to be studied and developed in future research in different countries at the various environments and different smartphone features. For example, one of the shortcomings of using smartphone-based sensors technology is the collected data is not directly collected from the pavement surface but is inferred from the data that resulted from the interaction among the vehicle, driver and pavement. This data processing could create limitations on the accuracy of such technology. It is also expected that data generated by sensors will vary according to the smartphone properties, sensor conditions, behavior of drivers, vehicle dynamics and conditions that lead to differences in recorded data. Therefore, such technology still needs further investigations and evaluations, especially in data collection accuracy. This review is expected to help in understanding the existing development, motivations, challenges, research gaps and future directions in the application of smartphones for PCM.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pavement Engineerin

Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review

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.Pavement Engineerin

Modelling and Optimization for Mortar Compressive Strength Incorporating Heat-Treated Fly Oil Shale Ash as an Effective Supplementary Cementitious Material Using Response Surface Methodology

Fly oil shale ash (FOSA) is a waste material known for its pozzolanic activity. This study intends to investigate the optimum thermal treatment conditions to use FOSA efficiently as a cement replacement material. FOSA samples were burned in an electric oven for 2, 4, and 6 h at temperatures ranging from 550 °C to 1000 °C with 150 °C intervals. A total of 333 specimens out of 37 different mixes were prepared and tested with cement replacement ratios between 10% and 30%. The investigated properties included the mineralogical characteristics, chemical elemental analysis, compressive strength, and strength activity index for mortar samples. The findings show that the content of SiO2 + Al2O3 + Fe2O3 was less than 70% in all samples. The strength activity index of the raw FOSA at 56 days exceeded 75%. Among all specimens, the calcined samples for 2 h demonstrated the highest pozzolanic activity and compressive strength with a 75% strength activity index. The model developed by RSM is suitable for the interpretation of FOSA in the cementitious matrix with high degrees of correlation above 85%. The optimal compressive strength was achieved at a 30% replacement level, a temperature of 700 °C for 2 h, and after 56 days of curing

Rheological modeling and microstructural evaluation of oily sludge modified bitumen

The disposal of oily sludge (OS) and depletion of crude oil reservoirs has become a global problem for the petroleum industries. The rising cost of bituminous materials is also attributed to the depletion of crude oil reservoirs. Incorporating OS in the production of bitumen can reduce waste generation, thereby ensuring sustainability in petroleum industries. This study carried out a series of physicochemical, rheological and morphological tests to examine the suitability of OS as a bitumen modifier. OS and binders were subjected to Fourier transform infrared spectroscopy (FTIR) and atomic force microscopy (AFM) to characterize their morphological and chemical configuration. Using a dynamic shear rheometer (DSR) and response surface methodology (RSM), the performance characteristics of binders were analyzed and modeled. FTIR showed the identical functional groups responsible for the compatibility of OS and bitumen. Fit statistics and diagnostic graphs showed the significance and adequacy of the quadratic model for rutting and fatigue factors. Response surface plots showed that OS improved the fatigue resistance of bitumen. The OS up to 8 % addition improves fatigue resistance while keeping the performance grade (PG) identical to that of base bitumen

Computational modelling for predicting rheological properties of composite modified asphalt binders

The complicated viscoelastic characteristics of asphalt binders make it a challenging task to precisely predict their rheological behavior. This study aims to investigate and compare the suitability of response surface methodology (RSM) and machine learning (ML) modeling approaches in predicting the complex modulus (G*), phase angle (δ), and rutting parameter (G*/sinδ) of Nano Silica (NS) and/or waste denim fiber (WDF) modified asphalt binders before and after short-term aging. To achieve this, an experimental scheme was designed for RSM and ML modeling with three variables including NS contents (0–6%), WDF contents (0–6%), and testing temperature (40–76 °C) as the inputs, and provided the G*, δ and G*/sinδ before and after short-term aging as the outputs. A wide range of ML algorithms was evaluated to determine the optimum ML model that can be used to accurately predict the rheological properties of NS/WDF-modified asphalt binders. RSM analysis results indicated that the G*, δ, and G*/sinδ of NS/WDF composite asphalt are significantly affected by the %NS, %WDF, and test temperatures. The RSM-developed models showed coefficient of determination (R2) values exceeding 0.97 for all responses, indicating adequate agreement between experimental results and models developed by RSM. From ML algorithms optimization and among all evaluated ML models, it was found that Gaussian process regression (GPR) exhibited the highest R2 with a value of (0.99) and the lowest Root Mean Square Error (RMSE) with a value of approximately 1%. The performance evaluation of the GPR model for predicting all responses showed a very small difference between the predicted and experimental results, highlighting the prediction accuracy of the developed ML models