Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Sustainable Method for Determining Shear Strength Parameters by Machine Learning
https://orcid.org/https://orcid.org/0009-0001-2244-0985
https://orcid.org/https://orcid.org/0000-0002-7496-3171
The conventional methods for determining the shear strength parameters of soil, namely cohesion (C) and angle of internal friction (φ), involve time-consuming and expensive machinery. Also, the extraction metal ore processing into metal and machine manufacturing involves a high level of carbon emission. During the operation of these machines a large quantity of electricity is generated in thermal power plants, leading to an indirect increase in the carbon footprint, thus suggesting a need for the adoption of more sustainable practices. This study is aimed at reducing net zero emission by laboratory testing machines by develop a predictive machine-learning model for estimating soil shear strength parameters by utilizing the existing soil data. An artificial neural network (ANN) is one such model inspired by the human brain, with the ability to learn complex patterns and relationships in data. For this, 88 soil samples in all were gathered from the literature that was available. Basic index properties of soil are used as inputs and C, φ are predicted as outputs. A neural network is developed using a Bayesian regularization optimization algorithm. The model developed is evaluated using the root mean squared error, coefficient of determination, and mean absolute error. The efficacy demonstrated by the ANN model ensures reliable predictions, thus promoting the adoption of these predictive models, leading to net zero carbon emissions in the testing field of geotechnical engineering.
Sustainable Method for Determining Shear Strength Parameters by Machine Learning
https://orcid.org/https://orcid.org/0009-0001-2244-0985
https://orcid.org/https://orcid.org/0000-0002-7496-3171
The conventional methods for determining the shear strength parameters of soil, namely cohesion (C) and angle of internal friction (φ), involve time-consuming and expensive machinery. Also, the extraction metal ore processing into metal and machine manufacturing involves a high level of carbon emission. During the operation of these machines a large quantity of electricity is generated in thermal power plants, leading to an indirect increase in the carbon footprint, thus suggesting a need for the adoption of more sustainable practices. This study is aimed at reducing net zero emission by laboratory testing machines by develop a predictive machine-learning model for estimating soil shear strength parameters by utilizing the existing soil data. An artificial neural network (ANN) is one such model inspired by the human brain, with the ability to learn complex patterns and relationships in data. For this, 88 soil samples in all were gathered from the literature that was available. Basic index properties of soil are used as inputs and C, φ are predicted as outputs. A neural network is developed using a Bayesian regularization optimization algorithm. The model developed is evaluated using the root mean squared error, coefficient of determination, and mean absolute error. The efficacy demonstrated by the ANN model ensures reliable predictions, thus promoting the adoption of these predictive models, leading to net zero carbon emissions in the testing field of geotechnical engineering.
Sustainable Method for Determining Shear Strength Parameters by Machine Learning
https://orcid.org/https://orcid.org/0009-0001-2244-0985
https://orcid.org/https://orcid.org/0000-0002-7496-3171
The conventional methods for determining the shear strength parameters of soil, namely cohesion (C) and angle of internal friction (φ), involve time-consuming and expensive machinery. Also, the extraction metal ore processing into metal and machine manufacturing involves a high level of carbon emission. During the operation of these machines a large quantity of electricity is generated in thermal power plants, leading to an indirect increase in the carbon footprint, thus suggesting a need for the adoption of more sustainable practices. This study is aimed at reducing net zero emission by laboratory testing machines by develop a predictive machine-learning model for estimating soil shear strength parameters by utilizing the existing soil data. An artificial neural network (ANN) is one such model inspired by the human brain, with the ability to learn complex patterns and relationships in data. For this, 88 soil samples in all were gathered from the literature that was available. Basic index properties of soil are used as inputs and C, φ are predicted as outputs. A neural network is developed using a Bayesian regularization optimization algorithm. The model developed is evaluated using the root mean squared error, coefficient of determination, and mean absolute error. The efficacy demonstrated by the ANN model ensures reliable predictions, thus promoting the adoption of these predictive models, leading to net zero carbon emissions in the testing field of geotechnical engineering.
Sustainable Method for Determining Shear Strength Parameters by Machine Learning
Lecture Notes in Civil Engineering
Kioumarsi, Mahdi (Herausgeber:in) / Shafei, Behrouz (Herausgeber:in) / Chorapalli, Jnanendra Vijay Kumar (Autor:in) / Das, Soukat Kumar (Autor:in)
The International Conference on Net-Zero Civil Infrastructures: Innovations in Materials, Structures, and Management Practices (NTZR) ; 2024 ; Oslo, Norway
The 1st International Conference on Net-Zero Built Environment ; Kapitel: 120 ; 1437-1450
09.01.2025
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Determining characteristic sand shear parameters of strength via a direct shear test
| BASE | 2016
Determining characteristic sand shear parameters of strength via a direct shear test
| BASE | 2016
Machine Learning-Based Prediction of Shear Strength Parameters of Rock Materials
| Springer Verlag | 2024
Determining characteristic sand shear parameters of strength via a direct shear test
| DOAJ | 2016
A Method of Data Processing for Determining Shear Strength Parameters of Rock
| British Library Conference Proceedings | 2011