Assessment of Marshall Stability of asphalt concrete with plastic waste using soft computing techniques

被引:5
|
作者
Kumar, Bhupender [1 ]
Kumar, Navsal [1 ]
机构
[1] Shoolini Univ, Dept Civil Engn, Solan 173229, Himachal Prades, India
关键词
Artificial neural network; Random Tree; Random Forest; Marshall Stability and plastic waste; PREDICTION;
D O I
10.1007/s41939-023-00180-x
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Asphalt concrete has long been the major material used in pavement engineering. As a result, improving the quality of asphalt concrete for better performance and longer durability has remained a research priority. The purpose of this research is to evaluate the potential of soft computing-based models for predicting the Marshall Stability of plastic waste asphalt mix, such as Random Forest (RF), Random Tree (RT), Bagging RF, Bagging RT, and artificial neural networks (ANN)-based models. Three statistical indices are used to assess each model's performance with various input variables, such as bitumen content (BC), plastic content (PC), bitumen grade (VG), and plastic size (PS), to derive the Marshall Stability (MS). The efficacy of the used models was evaluated using three statistical indices: the coefficient of correlation (CC), the mean absolute error (MAE), and root-mean-square error (RMSE). With CC values of 0.942 and 0.8957, MAE values of 1.0591 and 1.4736, and RMSE values of 1.5121 and 2.2225 for both the training and testing stages, the performance evaluation results showed that the RF-based model outperformed all other models for predicting the Marshall Stability (MS) of asphalt concrete using plastic waste. The MS of the asphalt mix is a key outcome of the sensitivity analysis pointing to the size of the plastic as an important parameter in the case of plastic waste.
引用
收藏
页码:733 / 745
页数:13
相关论文
共 50 条
  • [21] Determination of composite modulus of concrete containing reclaimed asphalt pavement using micromechanical modelling and soft computing techniques
    Deng, Yong
    Shi, Xijun
    Zhang, Yao
    JOURNAL OF CLEANER PRODUCTION, 2021, 327
  • [22] Permanent deformation analysis of asphalt mixtures using soft computing techniques
    Mirzahosseini, Mohammad Reza
    Aghaeifar, Alireza
    Alavi, Amir Hossein
    Gandomi, Amir Hossein
    Seyednour, Reza
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (05) : 6081 - 6100
  • [23] Marshall stability and flow of lime-modified asphalt concrete
    Ogundipe, Olumide Moses
    TRANSPORT RESEARCH ARENA TRA2016, 2016, 14 : 685 - 693
  • [24] Prediction of the marshall stability of reinforced asphalt concrete with steel fiber using fuzzy logic
    Serin, Sercan
    Morova, Nihat
    Saltan, Mehmet
    Terzi, Serdal
    Karasahin, Mustafa
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2014, 26 (04) : 1943 - 1950
  • [25] Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete
    Almohammed, Fadi
    Sihag, Parveen
    Sammen, Saad Sh.
    Ostrowski, Krzysztof Adam
    Singh, Karan
    Prasad, C. Venkata Siva Rama
    Zajdel, Paulina
    MATERIALS, 2022, 15 (02)
  • [26] Concrete materials compressive strength using soft computing techniques
    Lu, Chongyang
    MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN, 2023, 7 (2) : 1209 - 1221
  • [27] Concrete materials compressive strength using soft computing techniques
    Chongyang Lu
    Multiscale and Multidisciplinary Modeling, Experiments and Design, 2024, 7 : 1209 - 1221
  • [28] Assessment of Plastic Waste Modification to Asphalt Materials Using the Wet Process
    Bennert, Thomas
    Ericson, Christopher
    Haas, Edwin
    Wass, Edward
    Tulanowski, Drew
    Cytowicz, Nicholas
    TRANSPORTATION RESEARCH RECORD, 2024,
  • [29] Artificial neural network based modelling of the Marshall Stability of asphalt concrete
    Ozgan, Ercan
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (05) : 6025 - 6030
  • [30] Assessment of flexural strength of concrete with marble powder applying soft computing techniques
    Sharma N.
    Thakur M.S.
    Upadhya A.
    Sihag P.
    Journal of Building Pathology and Rehabilitation, 2023, 8 (1)