Muslich Hartadi Sutanto’s research while affiliated with Universiti Teknologi Petronas and other places

The asphalt pavement industry is transforming because of the growing influence of artificial intelligence and industrial digitization. As a result of this shift, there is a stronger emphasis on advanced statistical approaches like optimization tools like response surface methodology (RSM) and machine learning (ML) techniques. The goal of this paper is to provide a scientometric and systematic review of the application of RSM and ML applications in data-driven approaches such as optimizing, modeling, and predicting asphalt pavement performance to achieve sustainable asphalt pavements in support of numerous sustainable development goals (SDGs). These include Goals 9 (sustainable infrastructure), 11 (urban resilience), 12 (sustainable construction strategies), 13 (climate action through optimized materials), and 17 (multidisciplinary interaction). A thorough search of the ScienceDirect, Web of Science, and Scopus databases from 2010 to 2023 yielded 1249 relevant records, with 125 studies closely examined. Over the last thirteen years, there has been significant research growth in RSM and ML applications, particularly in ML-based pavement optimization. The study shows that the topic has a global presence, with notable contributions from Asia, North America, Europe, and other continents. Researchers have concentrated on utilizing sophisticated ML models such as support vector machines (SVM), artificial neural networks (ANN), and Bayesian networks for prediction. Also, the integration of RSM and ML provides a faster and more efficient method for analyzing large datasets to optimize asphalt pavement performance variables. Key contributors include the United States, China, and Malaysia, with global efforts focused on sustainable materials and approaches to reduce impact on the environment. Furthermore, the review demonstrates the integrated use of RSM and ML as transformative tools for improving sustainability, which contributes significantly to SDGs 9, 11, 12, 13, and 17. Providing valuable insights for future research and guiding decision-making for soft computing applications for asphalt pavement projects.

Coconut products such as oil, milk powder, activated carbon, and desiccated coconut are increasingly in demand, leading to higher coconut production and a surplus of coconut fibers. Despite their excellent physical and mechanical properties, these fibers are often discarded or burned due to limited research into alternative uses, contributing to environmental pollution. This study evaluates the potential of coconut fibers in hot mix asphalt (HMA) to reduce waste and enhance their mechanical performance. Central composite design (CCD) was adopted to optimize fiber-modified HMA mixes using response surface methodology (RSM) based on Marshall testing. Sixty Marshall samples with varying fiber content, bitumen content, and fiber length were prepared to develop the RSM model based on 20 runs. Fiber content (%), fiber length (mm), and bitumen content (%) were considered as factors, while marshall stability (KN) and flow (mm) were taken as responses. The optimized mix, containing 0.28% coconut fibers (approximately 13 mm in length) and 4. 16% bitumen, achieved a marshall stability of 18. 02 kN and a flow of 3.12 mm. Validation of the optimized solution with the experimental trials showed an error of 7.05% for marshall stability and 6. 11% for marshall flow. Indirect tensile strength testing showed a 5% higher tensile strength for the optimized dry mix compared to the 1.29 KN observed for control samples. Furthermore, the tensile strength ratio between dry and wet samples was recorded to be higher than the threshold of 80% for both control and optimized HMA mixes. Moreover, the indirect tensile stiffness modulus (ITSM) for control samples recorded at 5 °C was higher than the optimized mix. However, the optimized HMA mixes resulted in around 13%, 6%, and 2.16% higher ITSM at 15 °C, 20 °C, and 25 °C, respectively, in reference to the control mix. Furthermore, the indirect tensile fatigue testing revealed that the control mix performed better than the optimized mix. Nonetheless, the optimized mix showed steady behavior against stress variation as compared to the control mix. Overall, this study demonstrates the effective use of RSM to optimize the Marshall mix design, reducing laboratory testing. Additionally, it was observed that optimized fiber-modified HMA mixes exhibit superior mechanical properties compared to control samples, paving the roads for sustainable and efficient asphalt technologies.

The Marshall stability and flow of asphalt mixes are key performance indicators of their durability and suitability for use in the pavement industry. Achieving the optimal bitumen content and volumetric properties through mix design is critical and depends on the characteristics of the materials used. Recycling waste materials in asphalt is also vital for promoting environmental sustainability. The development of machine learning models plays a crucial role in predicting the performance of such asphalt mixes. This study explores the use of a machine learning approach to predict the performance of waste tyre metal fibre-modified asphalt mixes. A dataset consisting of 75 experimental data points from various mix proportions was compiled to train and test the model. The study used 60/70 penetration grade bitumen and five modified mixes with waste tyre metal fibre (WTMF) contents of 0%, 0.375%, 0.75%, 1.125%, and 1.5%. Decision tree regression was effectively employed to establish the relationship between the input variables. The predictive ability of the model was assessed using R-squared, adjusted R-squared, and mean absolute error. The input parameters included fibre content, bitumen content, aggregate percentage, and porosity. Analysis of the input variables showed that stability decreased while flow increased with higher fibre and bitumen contents. With an R² of 0.901 for training and 0.937 for testing phases, decision tree regression proved to be an effective model for predicting the performance of these modified asphalt mixes.

The Urban Heat Island (UHI) phenomenon, in which the temperature in urban regions is higher relative to that in surrounding rural areas, is primarily attributed to human activities and urban development. This temperature escalation poses various challenges, impacting energy consumption, human well-being, and the overall urban environment. This study explores the potential of phase change materials (PCMs) as an addition to asphaltic mixes in alleviating the UHI effect. PCMs can store and release thermal energy through phase transition and exhibit valuable thermal properties for temperature management. In the context of UHI, PCMs play a pivotal role in alleviating the surplus heat amassed in urban areas. This study evaluates the effectiveness of shape-stabilized PCM-impregnated aggregates to reduce pavement temperature. The mechanical and thermal performance of the four different bituminous mixes has been evaluated in this study. A decrease in marshall stability while an increase in marshall flow has been observed with the increase in the proportion of PCM-impregnated aggregates in the mix. Based on the thermal analysis of PCM-modified mixes, a temperature drop of 5.03 °C and 2.9 °C in core temperature was observed in the case of 70% and 35% replacement with PCM-impregnated aggregates. The introduction of PCM-impregnated aggregates into the mix lowers the rate of increase and decrease in temperature of the asphaltic mix in direct sunlight. Electrical resistivity increased by around 4% and 8.6% for 35% and 70% replacement as compared to control sample.

Malaysia's economic prosperity is overshadowed by a concerning rate of 19 daily road fatalities. This study aimed to investigate road users' perceptions of road service quality (RSQ) and its association with road traffic accidents (RTAs) on an expressway in Malaysia. A questionnaire-based approach collected data from respondents comprising bikers, motorists, bus operators, and truck drivers. Descriptive analysis indicated that, except for motorcyclists, most road user groups rated the overall RSQ of the expressway as poor. Statistical analysis revealed significant variations in perceptions of road surface among road user categories. Pearson correlation analysis demonstrated strong positive relationships between road surface, road drainage, road maintenance, and RTAs. No significant relationships were found between road furniture, rest areas, and RTAs. Multiple regression analysis revealed that road maintenance, road surface, and road drainage accounted for 7.6% of the variance in RTAs, highlighting their importance as predictors. The Relative Importance Index analysis identified ten influential factors on RTAs, including permanent wave, poor workmanship, water pounding, road settlement, repeated construction, invisible road markings, insufficient traffic signs, potholes and bumps, insufficient street lighting, and oily road surfaces. These findings provide policymakers with valuable insights to enhance road safety regulations and develop effective strategies for improving RSQ and reducing RTAs.

Currently, the viscoelastic properties of standard asphalt cement cannot sustain the increasing demands resulting from heavier traffic loads, greater stress levels, and changing environmental conditions. Thus, the usage of modifiers is encouraged. Also, the Sustainable Development Goal (SDG) encourages the use of waste resources and emerging technologies in asphalt pavement technology. This study intends to harness this gap by examining the use of oil palm clinker (OPC) as an asphalt-cement modifcation to improve its viscoelastic properties using an innovative prediction approach. The modifed asphalt-cement was produced by varying the acid-treated OPC powder (OPCP) content at 2%, 4%, 6%, and 8% and the rutting and fatigue performance was evaluated. This paper also presents an optimization approach and prediction comparison based on statistical modeling and artificial intelligence (AI) algorithms for the fatigue and rutting parameters of the modified asphalt cement. Model variables for the predictive models include OPCP content and test temperatures. The AI algorithms use 70% of the data for training, 15% for testing, and 15% for validation. The results showed that the incorporation of OPCP improves the properties of pure asphalt-cement by increasing stiffness and temperature susceptibility and that the crystalline phase of Si–O formed a novel structural group Si-OH. The RSM R2 for rutting for unaged and RTFO aged responses was (99.743 and 99.893), the RMSE was (436.210 and 954.945), and the MRE was 3.269 and 2.315) for the model statistical performance index, respectively. The ANN R2 for rutting for unaged and RTFO aged responses were (99.903 and 99.970) the RMSE (106.283 and 528.500) and MRE (1.759 and 1.039). PAV fatigue RSM R2 values were (99.984), RMSE (77979.750), and MRE (12.089), while ANN R2 values were (99.997), RMSE (53933.500), and MRE (5.262). The findings demonstrated that the generated model and algorithm could predict the fatigue and rutting performance of the OPCP-modifed asphalt cement accurately with the AI algorithms model outperforming the statistical model. Also, the study aligns with SDG 9 by developing advanced modeling techniques and enhancing infrastructure durability through innovative use of modified materials as well as SDG 12 by incorporating recycled materials into sustainable production practices.

Currently, the viscoelastic properties of conventional asphalt cement need to be improved to meet the increasing demands caused by larger traffic loads, increased stress, and changing environmental conditions. Thus, using modifiers is suggested. Furthermore, the Sustainable Development Goals (SDGs) promote using waste materials and new technologies in asphalt pavement technology. The present study aims to fill this gap by investigating the use of pulverized oil palm industry clinker (POPIC) as an asphalt–cement modifier to improve the fatigue life of bituminous concrete using an innovative prediction approach. Thus, this study proposes an approach that integrates statistically based machine learning approaches and investigates the effects of applied stress and temperature on the fatigue life of POPIC-modified bituminous concrete. POPIC-modified bituminous concrete (POPIC-MBC) is produced from a standard Marshall mix. The interactions between POPIC concentration, stress, and temperature were optimized using response surface methodology (RSM), resulting in 7.5% POPIC, 11.7 °C, and 0.2 MPa as the optimum parameters for fatigue life. To improve the prediction accuracy and robustness of the results, RSM and ANN models were used and analyzed using MATLAB and JMP Pro, respectively. The performance of the developed model was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE). The study found that using RSM, MATLAB, and JMP Pro resulted in a comprehensive analysis. MATLAB achieved an R² value of 0.9844, RMSE of 3.094, and MRE of 312.427, and JMP Pro achieved an R² value of 0.998, RMSE of 1.245, and MRE of 126.243, demonstrating higher prediction accuracy and superior performance than RSM, which had an R² value of 0.979, RMSE of 3.757, and MRE of 357.846. Further validation with parity, Taylor, and violin plots demonstrates that both models have good prediction accuracy, with the JMP Pro ANN model outperforming in terms of accuracy and alignment. This demonstrates the machine learning approach’s efficiency in analyzing the fatigue life of POPIC-MBC, revealing it to be a useful tool for future research and practical applications. Furthermore, the study reveals that the innovative approach adopted and POPIC modifier, obtained from biomass waste, meets zero-waste and circular bioeconomy goals, contributing to the UN’s SDGs 9, 11, 12, and 13.

The goals of this study are to assess the viability of waste tire-derived char (WTDC) as a sustainable, low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network (SCNN) model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC. The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDC-modified asphalt mixtures (WTDC-MAM). The input variables comprised waste tire char content and asphalt binder content. The output variables comprised mixture unit weight, total voids, voids filled with asphalt, Marshall stability, and flow. Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures. For predictive modeling, the SCNN model is employed, incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability. The optimal network architecture, using the collected dataset, was a 2:6:5 structure, and the neural network was trained with 60% of the data, whereas the other 20% was used for cross-validation and testing respectively. The network employed a hyperbolic tangent (tanh) activation function and a feed-forward backpropagation. According to the results, the network model could accurately predict the volumetric and Marshall properties. The predicted accuracy of SCNN was found to be as high value >98% and low prediction errors for both volumetric and Marshall properties. This study demonstrates WTDC's potential as a low-cost, sustainable aggregate replacement. The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

Plain asphalt typically is an insulator to the flow of electric current. It can be modified to conductive asphalt by adding various recyclable and environment-friendly conductive additives in it. Such asphalt can provide smart and multifunctional environmentally sustainable applications in the pavement industry. Its production and performance behavior parameters are, however, yet to be entirely understood. This study presents a review of literature on conductive asphalt using systematic literature review and scientometric analysis techniques to holistically understand conductive asphalt and current research developments in this field. The objective was to perform a critical review and scientometrically characterize the published research studies. Literature was acquired from credible research databases for the study duration from 2009 to 2022, and these were subsequently using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol to identify the most relevant documents. Sixty-two bibliographic articles were consequently selected for the study. Systematic review identified the research themes and techniques adopted in the field of conductive asphalt technology, and the scientometric analysis quantified the characteristics of the articles. VOSViewer was utilized for visualizing the key findings of the quantitative analysis. Development of conductive asphalt has great research potential and improving its piezoresistivity and conductive network is the future research focus of smart asphalt technology. An experimental study was also conducted, and the results were presented. A dataset of 75 Marshall asphalt specimens of various mix proportions was compiled to assess the Marshall parameters, volumetrics, and their electrical resistivity. 60/70 penetration grade bitumen was used along with five waste tire metal fiber based modified asphalt mixes with contents 0%, 0.375%, 0.75%, 1.125%, and 1.5%. This review, along with experimental investigations, provided an in-depth understanding of conductive asphalt concrete’s behavior, the emerging trends to support future studies, and helped to identify the current major research themes and the corresponding challenges.

Plain asphalt typically is an insulator to the flow of electric current. It can be modified to conductive asphalt by adding various recyclable and environment-friendly conductive additives in it. Such asphalt can provide smart and multifunctional environmentally sustainable applications in the pavement industry. Its production and performance behavior parameters are, however, yet to be entirely understood. This study presents a review of literature on conductive asphalt using systematic literature review and scientometric analysis techniques to holistically understand conductive asphalt and current research developments in this field. The objective was to perform a critical review and scientometrically characterize the published research studies. Literature was acquired from credible research databases for the study duration from 2009 to 2022, and these were subsequently using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol to identify the most relevant documents. Sixty-two bibliographic articles were consequently selected for the study. Systematic review identified the research themes and techniques adopted in the field of conductive asphalt technology, and the scientometric analysis quantified the characteristics of the articles. VOSViewer was utilized for visualizing the key findings of the quantitative analysis. Development of conductive asphalt has great research potential and improving its piezoresistivity and conductive network is the future research focus of smart asphalt technology. An experimental study was also conducted, and the results were presented. A dataset of 75 Marshall asphalt specimens of various mix proportions was compiled to assess the Marshall parameters, volumetrics, and their electrical resistivity. 60/70 penetration grade bitumen was used along with five waste tire metal fiber based modified asphalt mixes with contents 0%, 0.375%, 0.75%, 1.125%, and 1.5%. This review, along with experimental investigations, provided an in-depth understanding of conductive asphalt concrete’s behavior, the emerging trends to support future studies, and helped to identify the current major research themes and the corresponding challenges.