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Predictive modelling of volumetric and Marshall properties of asphalt mixtures modified with waste tire-derived char:A statistical neural

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Journal of Road Engineering 2024年第3期4卷 318-333页

作者： Nura Shehu Aliyu Yaro Muslich Hartadi Sutanto Noor Zainab Habib Aliyu Usman Abiola Adebanjo Surajo Abubakar Wada ahmad hussaini jagaba Department of Civil and Environmental Engineering Universiti Teknologi PETRONASSeri Iskandar 32610Malaysia Department of Civil Engineering Ahmadu Bello UniversityZaria 810107Nigeria Institute of Infrastructure and Environment Heriot-Watt UniversityDubai 294345United Arab Emirates School of Civil Engineering Central South UniversityChangsha 410075China Interdisciplinary Research Centre for Membranes and Water Security King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia

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 *** study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDCmodified asphalt mixtures(WTDC-MAM).The input variables comprised waste tire char content and asphalt binder *** output variables comprised mixture unit weight,total voids,voids filled with asphalt,Marshall stability,and *** coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt *** predictive modeling,the SCNN model is employed,incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and *** 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 *** network employed a hyperbolic tangent(tanh)activation function and a feed-forward *** to the results,the network model could accurately predict the volumetric and Marshall *** predicted accuracy of SCNN was found to be as high value>98%and low prediction errors for both volumetric and Marshall *** study demonstrates WTDC's potential as a low-cost,sustainable aggregate *** SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

关键词： Waste tire Neural network Sustainable practices Asphalt mixtures Predictive model

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Predictive modelling of volumetric and Marshall properties of asphalt mixtures modified with waste tire-derived char: A statistical neural network approach

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Journal of Road Engineering 2024年第03期 318-333页

作者： Nura Shehu Aliyu Yaro Muslich Hartadi Sutanto Noor Zainab Habib Aliyu Usman Abiola Adebanjo Surajo Abubakar Wada ahmad hussaini jagaba Department of Civil and Environmental Engineering Universiti Teknologi PETRONAS Department of Civil Engineering Ahmadu Bello University Institute of Infrastructure and Environment Heriot-Watt University School of Civil Engineering Central South University Interdisciplinary Research Centre for Membranes and Water Security King Fahd University of Petroleum and Minerals

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.

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Application and circular economy prospects of palm oil waste for eco-friendly asphalt pavement industry:A review

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Journal of Road Engineering 2022年第4期2卷 309-331页

作者： Nura Shehu Aliyu Yaro Muslich Hartadi Sutanto Noor Zainab Habib Madzlan Napiah Aliyu Usman ahmad hussaini jagaba Abdulnaser M.Al-Sabaeei Department of Civil and Environmental Engineering Universiti Teknologi PETRONASBandar Seri Iskandar 32610Malaysia Department of Civil Engineering Ahmadu Bello UniversityZaria 810107Nigeria Institute of Self-Sustainable Building Universiti Teknologi PETRONASBandar Seri Iskandar 32610Malaysia School of Energy GeoscienceInfrastructureand SocietyHeriot-Watt University DubaiDubai 38103United Arab Emirates

During the production of palm oil,a significant amount of waste is ***,because of inefficient handling and utilization,these wastes are becoming a larger *** a result,one initiative is to use these wastes in the pavement industry as sustainable ***,there is still a lack of understanding about the wider incorporation of palm oil waste in asphalt pavement and its *** study examines existing literature on the use of various wastes in the pavement industry,including palm oil clinker(POC),palm oil fibre(POF),palm kernel shell(PKS),and palm oil fuel ash(POFA).As a result,this paper presents a systematic review and scientometric investigation of related study publications on many uses of palm oil waste in the asphalt pavement industry and its performance from 2009 to *** VOS viewer application was used to conduct the sciento-metric study *** relationship between interactions detected in co-authored country studies cited sources of co-citation,and the keyword of the co-occurrence and publication source enabled the identification of the research *** to the systematic literature review,40%–60% POC can be used to fine aggregate for optimal performance,while 0–100%PKS can be used to replace coarse *** addition,50%–80% POFA or POC fine(POCF)can be used as a filler replacement,5%–8% POCF or POFA as a bitumen modifier,and 0.3% POF as a stabilizing ***,the study demonstrates that the safety of utilizing wastes with more than 50% CO_(2) emissions can be curtailed with minimal heavy metal leaching and radioactivity *** scientometric analysis may encourage researchers to seek out gaps in the literature that will aid in the long-term,multifaceted use of palm oil wastes in the asphalt pavement ***,the study recommends employing and researching the enormous potential of using palm oil waste in the pavement sectors because they are more sustainable and have better perfor

关键词： Asphalt pavement Circular economy Eco-friendly Palm oil waste VOSviewer

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