Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles
The resilient modulus (M R ) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the M R , although it is expensive, time-consuming, and requires s...
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| Published in | Scientific reports Vol. 14; no. 1; pp. 27928 - 28 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
13.11.2024
Nature Publishing Group Nature Portfolio |
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| Abstract | The resilient modulus (M
R
) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the M
R
, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the M
R
of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (
σ
d
), and confining stress (
σ
3
). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the
σ
d
parameter is the least significant factor in predicting the M
R
of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. |
|---|---|
| AbstractList | The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. The resilient modulus (M R ) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the M R , although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the M R of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σ d ), and confining stress (σ 3 ). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σ d parameter is the least significant factor in predicting the M R of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. The resilient modulus (M R ) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the M R , although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the M R of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress ( σ d ), and confining stress ( σ 3 ). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σ d parameter is the least significant factor in predicting the M R of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. Abstract The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σ d ), and confining stress (σ 3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σ d parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. The resilient modulus (M ) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the M , although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the M of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σ ), and confining stress (σ ). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σ parameter is the least significant factor in predicting the M of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. |
| ArticleNumber | 27928 |
| Author | Ouahbi, Tariq Khan, Beenish Jehan Qamar, Wajeeha Paul, Sonjoy EL-Gawaad, N. S. Abd González-Lezcano, Roberto Alonso Kashif, Muhammad Abdullah, Shahriar Abdullah, Gamil M. S. Al‑Zubi, Mohammad A. Ahmad, Mahmood |
| Author_xml | – sequence: 1 givenname: Mohammad A. surname: Al‑Zubi fullname: Al‑Zubi, Mohammad A. organization: Department of Mechanical Engineering, Hijjawai Faculty for Engineering, Yarmouk University – sequence: 2 givenname: Mahmood surname: Ahmad fullname: Ahmad, Mahmood email: ahmadm@uetpeshawar.edu.pk, ahmadm@uniten.edu.my organization: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Department of Artificial Intelligence, University of Engineering and Technology Peshawar (Bannu Campus) – sequence: 3 givenname: Shahriar surname: Abdullah fullname: Abdullah, Shahriar organization: Department of Civil and Environmental Engineering, Lamar University – sequence: 4 givenname: Beenish Jehan surname: Khan fullname: Khan, Beenish Jehan organization: Department of Civil Engineering, CECOS University of IT and Emerging Sciences – sequence: 5 givenname: Wajeeha surname: Qamar fullname: Qamar, Wajeeha organization: Department of Civil Engineering, Institute of Engineering and Fertilizer Research – sequence: 6 givenname: Gamil M. S. surname: Abdullah fullname: Abdullah, Gamil M. S. organization: Department of Civil Engineering, College of Engineering, Najran University – sequence: 7 givenname: Roberto Alonso surname: González-Lezcano fullname: González-Lezcano, Roberto Alonso organization: Department of Architecture and Design, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities – sequence: 8 givenname: Sonjoy surname: Paul fullname: Paul, Sonjoy organization: Department of Civil and Environmental Engineering, Lamar University – sequence: 9 givenname: N. S. Abd surname: EL-Gawaad fullname: EL-Gawaad, N. S. Abd organization: Muhayil Asir, Applied College, King Khalid University – sequence: 10 givenname: Tariq surname: Ouahbi fullname: Ouahbi, Tariq organization: LOMC, UMR 6294 CNRS, Université Le Havre Normandie, Normandie Université – sequence: 11 givenname: Muhammad surname: Kashif fullname: Kashif, Muhammad organization: Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus) |
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| Keywords | Pavements Stabilized base Long short-term memory networks Wet-dry cycles Resilient modulus Graphical user interface Resilient modulus Pavements Stabilized base Wet-dry cycles Long short-term memory networks Graphical user interface |
| Language | English |
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| Snippet | The resilient modulus (M
R
) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design... The resilient modulus (M ) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design... The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design... The resilient modulus (M R ) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design... Abstract The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design... |
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| SubjectTerms | 639/166 639/301 704/172 Accuracy Algorithms Aluminum oxide Artificial Intelligence Calcium oxide Civil Engineering College campuses Computer Science Datasets Engineering Sciences Ferric oxide Graphical user interface Humanities and Social Sciences Long short-term memory Long short-term memory networks Moisture content multidisciplinary Pavements Regression analysis Resilient modulus Science Science (multidisciplinary) Sensitivity analysis Silica Stabilized base Support vector machines Water content Wet-dry cycles |
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| Title | Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles |
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