Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms
Session
Civil Engineering, Infrastructure and Environment
Description
The usage of recycled aggregates in construction materials has received a lot of interest because of its potential to minimize environmental consequences and the loss of natural resources. The prediction of splitting tensile strength, a crucial mechanical attribute determining structural performance, is an integral part of assessing the feasibility of recycled aggregates for construction. Traditional techniques for evaluating the splitting tensile strength of recycled aggregates rely on advanced and time-consuming laboratory testing, which may be costly and inefficient for large- scale applications. This work proposes machine learning-based algorithms for predicting the performance of splitting tensile strength. In this research, 257 data points were collected from a previous study containing input variables affecting split tensile strength. Three methods were used to build different predictive models, i.e., support vector regression, XG boost, and random forest. The performance indices of various models were evaluated using metrics like MAE, RMSE, MAPE, and MASE to measure the models' accuracy and reliability. This research indicates that XG boost algorithms outperform other models with RMSE value of 1.26. The implementation of proposed models improves the reliability of predictions, allowing researchers to make informed decisions about incorporating recycled materials in sustainable construction practices, thereby contributing to the reduction of environmental impacts in the construction sector.
Keywords:
Splitting Tensile Strength, Machine Learning, Recycled Aggregates, Sustainability, Prediction
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-550-95-6
Location
UBT Lipjan, Kosovo
Start Date
28-10-2023 8:00 AM
End Date
29-10-2023 6:00 PM
DOI
10.33107/ubt-ic.2023.370
Recommended Citation
Parida, Lukesh Veloso de; Moharana, Sumedha Melo de; and Giri, Sourav Kumar, "Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms" (2023). UBT International Conference. 35.
https://knowledgecenter.ubt-uni.net/conference/IC/civil/35
Splitting Tensile Strength Prediction Using Machine Learning Based Optimization Algorithms
UBT Lipjan, Kosovo
The usage of recycled aggregates in construction materials has received a lot of interest because of its potential to minimize environmental consequences and the loss of natural resources. The prediction of splitting tensile strength, a crucial mechanical attribute determining structural performance, is an integral part of assessing the feasibility of recycled aggregates for construction. Traditional techniques for evaluating the splitting tensile strength of recycled aggregates rely on advanced and time-consuming laboratory testing, which may be costly and inefficient for large- scale applications. This work proposes machine learning-based algorithms for predicting the performance of splitting tensile strength. In this research, 257 data points were collected from a previous study containing input variables affecting split tensile strength. Three methods were used to build different predictive models, i.e., support vector regression, XG boost, and random forest. The performance indices of various models were evaluated using metrics like MAE, RMSE, MAPE, and MASE to measure the models' accuracy and reliability. This research indicates that XG boost algorithms outperform other models with RMSE value of 1.26. The implementation of proposed models improves the reliability of predictions, allowing researchers to make informed decisions about incorporating recycled materials in sustainable construction practices, thereby contributing to the reduction of environmental impacts in the construction sector.