Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach
Abstract This study investigates the effects of strength and durability of concrete for different water-cement ratios, aggregate contents, and partial replacement of biomedical waste ash at 5%, 10%, 15%, 20%, and 25% by weight of cement. At 7, 14, and 28 days, the control mix showed inferior mechani...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | Discover Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43939-025-00223-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850190992299261952 |
|---|---|
| author | Rakesh Kumar S. Karthik Abhishek Kumar Adithya Tantri Shahaji S. Sathvik |
| author_facet | Rakesh Kumar S. Karthik Abhishek Kumar Adithya Tantri Shahaji S. Sathvik |
| author_sort | Rakesh Kumar |
| collection | DOAJ |
| description | Abstract This study investigates the effects of strength and durability of concrete for different water-cement ratios, aggregate contents, and partial replacement of biomedical waste ash at 5%, 10%, 15%, 20%, and 25% by weight of cement. At 7, 14, and 28 days, the control mix showed inferior mechanical properties, particularly compressive strength, compared to concrete mixtures containing Biomedical Waste Ash (BWA). The replacement of cement by 5% and 10% increased the compressive strength but it is decreasing from 15%. Additionally, BWA modified concrete demonstrated a slower water absorption rate and minimal weight loss under acid test curing conditions, indicating enhanced durability. The economic and environmental benefits of incorporating biomedical waste into concrete promote sustainable construction practices. Using three machine learning approaches—K-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost—the compressive strength of concrete with biomedical waste ash was simulated. Cement, biomedical waste, water absorption, slump, and the water-to-cement ratio were key input variables. Among the models tested, the RF model emerged as the most accurate, with a predictive performance of R2 = 0.9945 and RMSE = 0.7080. Its unparalleled reliability, consistency, and accuracy in predicting compressive strength make it a top choice for this task. |
| format | Article |
| id | doaj-art-e01e5089cf8744fe927da2915eb6e04e |
| institution | OA Journals |
| issn | 2730-7727 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Materials |
| spelling | doaj-art-e01e5089cf8744fe927da2915eb6e04e2025-08-20T02:15:05ZengSpringerDiscover Materials2730-77272025-02-015112110.1007/s43939-025-00223-9Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approachRakesh Kumar0S. Karthik1Abhishek Kumar2Adithya Tantri3Shahaji4S. Sathvik5Department of Civil Engineering, Dayananda Sagar College of EngineeringDepartment of Civil Engineering, Dayananda Sagar College of EngineeringDepartment of Civil Engineering, Government Engineering College BankaDepartment of Civil Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationDepartment of Civil Engineering, Dayananda Sagar College of EngineeringDepartment of Civil Engineering, Dayananda Sagar College of EngineeringAbstract This study investigates the effects of strength and durability of concrete for different water-cement ratios, aggregate contents, and partial replacement of biomedical waste ash at 5%, 10%, 15%, 20%, and 25% by weight of cement. At 7, 14, and 28 days, the control mix showed inferior mechanical properties, particularly compressive strength, compared to concrete mixtures containing Biomedical Waste Ash (BWA). The replacement of cement by 5% and 10% increased the compressive strength but it is decreasing from 15%. Additionally, BWA modified concrete demonstrated a slower water absorption rate and minimal weight loss under acid test curing conditions, indicating enhanced durability. The economic and environmental benefits of incorporating biomedical waste into concrete promote sustainable construction practices. Using three machine learning approaches—K-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost—the compressive strength of concrete with biomedical waste ash was simulated. Cement, biomedical waste, water absorption, slump, and the water-to-cement ratio were key input variables. Among the models tested, the RF model emerged as the most accurate, with a predictive performance of R2 = 0.9945 and RMSE = 0.7080. Its unparalleled reliability, consistency, and accuracy in predicting compressive strength make it a top choice for this task.https://doi.org/10.1007/s43939-025-00223-9Biomedical wasteMechanical propertiesDurability propertiesPredictionRandom forestK-nearest neighbor |
| spellingShingle | Rakesh Kumar S. Karthik Abhishek Kumar Adithya Tantri Shahaji S. Sathvik Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach Discover Materials Biomedical waste Mechanical properties Durability properties Prediction Random forest K-nearest neighbor |
| title | Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach |
| title_full | Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach |
| title_fullStr | Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach |
| title_full_unstemmed | Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach |
| title_short | Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach |
| title_sort | machine learning approach for predicting the compressive strength of biomedical waste ash in concrete a sustainability approach |
| topic | Biomedical waste Mechanical properties Durability properties Prediction Random forest K-nearest neighbor |
| url | https://doi.org/10.1007/s43939-025-00223-9 |
| work_keys_str_mv | AT rakeshkumar machinelearningapproachforpredictingthecompressivestrengthofbiomedicalwasteashinconcreteasustainabilityapproach AT skarthik machinelearningapproachforpredictingthecompressivestrengthofbiomedicalwasteashinconcreteasustainabilityapproach AT abhishekkumar machinelearningapproachforpredictingthecompressivestrengthofbiomedicalwasteashinconcreteasustainabilityapproach AT adithyatantri machinelearningapproachforpredictingthecompressivestrengthofbiomedicalwasteashinconcreteasustainabilityapproach AT shahaji machinelearningapproachforpredictingthecompressivestrengthofbiomedicalwasteashinconcreteasustainabilityapproach AT ssathvik machinelearningapproachforpredictingthecompressivestrengthofbiomedicalwasteashinconcreteasustainabilityapproach |