Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica
It is evident that preparing materials, casting samples, curing, and testing all need time and money. The construction sector will benefit if these problems can be handled using cutting-edge techniques like machine learning. Also, a material’s ultrasonic pulse velocity (UPV) is affected by various v...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2022.1098304/full |
| _version_ | 1811290656107134976 |
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| author | Kaffayatullah Khan Muhammad Nasir Amin Umbreen Us Sahar Waqas Ahmad Kamran Shah Abdullah Mohamed |
| author_facet | Kaffayatullah Khan Muhammad Nasir Amin Umbreen Us Sahar Waqas Ahmad Kamran Shah Abdullah Mohamed |
| author_sort | Kaffayatullah Khan |
| collection | DOAJ |
| description | It is evident that preparing materials, casting samples, curing, and testing all need time and money. The construction sector will benefit if these problems can be handled using cutting-edge techniques like machine learning. Also, a material’s ultrasonic pulse velocity (UPV) is affected by various variables, and it is difficult to study their combined effect experimentally. This research used machine learning to assess the UPV and SHapley Additive ExPlanations techniques to study the impact of input parameters of hybrid fiber-reinforced concrete modified with nano-silica (HFRNSC). Three ML algorithms were employed, i.e., gradient boosting regressor, adaptive boosting regressor, and extreme gradient boosting, for ultrasonic pulse velocity evaluation. The accuracy of machine learning models was measured via the coefficient of determination (R2), k-fold analysis, statistical tests, and comparing the predicted and actual ultrasonic pulse velocity. This study determined that the gradient boosting and adaptive boosting models had a good level of accuracy for ultrasonic pulse velocity, but the extreme gradient boosting method estimated the ultrasonic pulse velocity of HFRNSCs with a greater degree of precision. Also, from the statistical checks and k-fold approach, it was discovered that the extreme gradient boosting method is more exact in estimating the ultrasonic pulse velocity of HFRNSCs. The SHapley Additive ExPlanations analysis revealed that the age of the specimen and nano-silica had a greater positive impact on the ultrasonic pulse velocity of HFRNSCs, whereas the coarse aggregate to fine aggregate ratio had a negative impact. In addition, fiber volume was found to have both positive and negative effects. By aiding the development of rapid and low-cost methods for determining material properties and the influence of input parameters, the construction industry may profit from the use of such technologies. |
| first_indexed | 2024-04-13T04:16:34Z |
| format | Article |
| id | doaj.art-5f01a57d60fc4c798ad90443cf063ec7 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-04-13T04:16:34Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-5f01a57d60fc4c798ad90443cf063ec72022-12-22T03:02:58ZengFrontiers Media S.A.Frontiers in Materials2296-80162022-12-01910.3389/fmats.2022.10983041098304Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silicaKaffayatullah Khan0Muhammad Nasir Amin1Umbreen Us Sahar2Waqas Ahmad3Kamran Shah4Abdullah Mohamed5Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi ArabiaCivil Engineering Department, University of Engineering and Technology, Lahore, PakistanDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi ArabiaResearch Centre, Future University in Egypt, New Cairo, EgyptIt is evident that preparing materials, casting samples, curing, and testing all need time and money. The construction sector will benefit if these problems can be handled using cutting-edge techniques like machine learning. Also, a material’s ultrasonic pulse velocity (UPV) is affected by various variables, and it is difficult to study their combined effect experimentally. This research used machine learning to assess the UPV and SHapley Additive ExPlanations techniques to study the impact of input parameters of hybrid fiber-reinforced concrete modified with nano-silica (HFRNSC). Three ML algorithms were employed, i.e., gradient boosting regressor, adaptive boosting regressor, and extreme gradient boosting, for ultrasonic pulse velocity evaluation. The accuracy of machine learning models was measured via the coefficient of determination (R2), k-fold analysis, statistical tests, and comparing the predicted and actual ultrasonic pulse velocity. This study determined that the gradient boosting and adaptive boosting models had a good level of accuracy for ultrasonic pulse velocity, but the extreme gradient boosting method estimated the ultrasonic pulse velocity of HFRNSCs with a greater degree of precision. Also, from the statistical checks and k-fold approach, it was discovered that the extreme gradient boosting method is more exact in estimating the ultrasonic pulse velocity of HFRNSCs. The SHapley Additive ExPlanations analysis revealed that the age of the specimen and nano-silica had a greater positive impact on the ultrasonic pulse velocity of HFRNSCs, whereas the coarse aggregate to fine aggregate ratio had a negative impact. In addition, fiber volume was found to have both positive and negative effects. By aiding the development of rapid and low-cost methods for determining material properties and the influence of input parameters, the construction industry may profit from the use of such technologies.https://www.frontiersin.org/articles/10.3389/fmats.2022.1098304/fullnano-silicafiber-reinforced concreteultrasonic pulse velocitymachine learningSHAP analysishybrid fibers |
| spellingShingle | Kaffayatullah Khan Muhammad Nasir Amin Umbreen Us Sahar Waqas Ahmad Kamran Shah Abdullah Mohamed Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica Frontiers in Materials nano-silica fiber-reinforced concrete ultrasonic pulse velocity machine learning SHAP analysis hybrid fibers |
| title | Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica |
| title_full | Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica |
| title_fullStr | Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica |
| title_full_unstemmed | Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica |
| title_short | Machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber-reinforced concrete modified with nano-silica |
| title_sort | machine learning techniques to evaluate the ultrasonic pulse velocity of hybrid fiber reinforced concrete modified with nano silica |
| topic | nano-silica fiber-reinforced concrete ultrasonic pulse velocity machine learning SHAP analysis hybrid fibers |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2022.1098304/full |
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