Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results
Keywords:Concrete compressive strength, early-age, machine learning, metaheuristic, prediction.
Estimating the accurate concrete strength has become a critical issue in civil engineering. The 28-day concrete cylinder test results depict the concrete's characteristic strength which was prepared and cast as part of the concrete work on the project. Waiting 28 days is important to guarantee the quality control of the procedure, even though it is a slow process. This research develops an advanced machine learning method to forecast the concrete compressive strength using the concrete mix proportion and early-age strength test results. Thirty-eight historical cases in total were used to create the intelligence prediction method. The results obtained indicate the effectiveness of the advanced hybrid machine learning strategy in forecasting the strength of the concrete with a comparatively high degree of accuracy calculated using 4 error indicators. As a result, the suggested study can provide a great advantage for construction project managers in decision-making procedures that depend on early strength results of the tests.
Kheder, G.F., Gabban, A.M.A., and Abid, S.M., Mathematical Model for the Prediction of Cement Compressive Strength at the Ages of 7 and 28 Days within 24 Hours, Materials and Structures, 36(10), 2003, pp. 693.
Cheng, M.-Y. and Prayogo, D., Symbiotic Organisms Search: A New Metaheuristic Optimization Algorithm, Computers & Structures, 139, 2014, pp. 98-112.
Cheng, M.-Y., Prayogo, D., and Wu, Y.-W., Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture, Journal of Computing in Civil Engineering, 28(4), 2014.
Cheng, M.-Y., Wibowo, D.K., Prayogo, D., and Roy, A.F.V., Predicting Productivity Loss Caused by Change Orders using the Evolutionary Fuzzy Support Vector Machine Inference Model, Journal of Civil Engineering and Management, 21(7), 2015, pp. 881-892.
Hoang, N.-D. and Pham, A.-D., Hybrid Artificial Intelligence Approach based on Metaheuristic and Machine Learning for Slope Stability Assessment: A Multinational Data Analysis, Expert Systems with Applications, 46, 2016, pp. 60-68.
Cheng, M.-Y., Prayogo, D., Ju, Y.-H., Wu, Y.-W., and Sutanto, S., Optimizing Mixture Properties of Biodiesel Production using Genetic Algorithm-based Evolutionary Support Vector Machine, International Journal of Green Energy, 13(15), 2016, pp. 1599-1607.
Hoang, N.-D., Tien Bui, D., and Liao, K.-W., Groutability Estimation of Grouting Processes with Cement Grouts using Differential Flower Pollination Optimized Support Vector Machine, Applied Soft Computing, 45, 2016, pp. 173-186.
Suykens, J.A.K. and Vandewalle, J., Least Squares Support Vector Machine Classifiers, Neural Processing Letter, 9(3), 1999, pp. 293-300.
Cheng, M.-Y. and Hoang, N.-D., Estimating Construction Duration of Diaphragm Wall using Firefly-Tuned Least Squares Support Vector Machine, Neural Computing and Applications, 2017, pp. 1-9.
Prayogo, D., Cheng, M.Y., Widjaja, J., Ongkowijoyo, H., and Prayogo, H. Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique. in ISARC 2017 - Proceedings of the 34th International Symposium on Automation and Robotics in Construction, 2017.
Panda, A. and Pani, S., A Symbiotic Organisms Search Algorithm with Adaptive Penalty Function to Solve Multi-Objective Constrained Optimization Problems, Applied Soft Computing, 46, 2016, pp. 344-360.
Verma, M., Thirumalaiselvi, A., and Rajasankar, J., Kernel-based Models for Prediction of Cement Compressive Strength, Neural Computing and Applications, 28(1), 2017, pp. 1083-1100.
Prayogo, D., An Innovative Parameter-Free Symbiotic Organisms Search (SOS) for Solving Construction-Engineering Problems, 2015, PhD thesis, Department of Construction Engineering, National Taiwan University of Science and Technology.
Cheng, M.-Y., Prayogo, D., and Tran, D.-H., Optimizing Multiple-Resources Leveling in Multiple Projects using Discrete Symbiotic Organisms Search, Journal of Computing in Civil Engineering, 30(3), 2016, pp. 04015036.
Tran, D.-H., Cheng, M.-Y., and Prayogo, D., A Novel Multiple Objective Symbiotic Organisms Search (MOSOS) for Time–Cost–Labor Utilization Tradeoff Problem, Knowledge-Based Systems, 94, 2016, pp. 132-145.
Prayogo, D., Cheng, M.-Y., and Prayogo, H., A Novel Implementation of Nature-Inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search, Civil Engineering Dimension, 19(1), 2017, pp. 36-43.
Yu, V.F., Redi, A.A.N.P., Yang, C.-L., Ruskartina, E., and Santosa, B., Symbiotic Organisms Search and Two Solution Representations for Solving the Capacitated Vehicle Routing Problem, Applied Soft Computing, 52, 2017, pp. 657-672.
Tejani, G.G., Savsani, V.J., Bureerat, S., and Patel, V.K., Topology and Size Optimization of Trusses with Static and Dynamic Bounds by Modified Symbiotic Organisms Search, Journal of Computing in Civil Engineering, 32(2), 2018, pp. 04017085.
Prayogo, D., Gosno, R.A., Evander, R., and Limanto, S., Implementasi Metode Metaheuristik Symbiotic Organisms Search Dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja, Jurnal Teknik Industri, 19(2), 2018, pp. 103-114.
Cheng, M.-Y., Chiu, C.-K., Chiu, Y.-F., Wu, Y.-W., Syu, Z.-L., Prayogo, D., and Lin, C.-H., SOS Optimization Model for Bridge Life Cycle Risk Evaluation and Maintenance Strategies, Journal of the Chinese Institute of Civil and Hydraulic Engineering, 26(4), 2014.
Kohavi, R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, in International Joint Conference on Artificial Intelligence, 1995, Stanford, CA.
Rafi, M. and Nasir, M., Models for Prediction of 28-Day Concrete Compressive Strength, Journal of Testing and Evaluation, 44(3), 2016, pp. 1217-1228.
Chang, C.-C. and Lin, C.-J., LIBSVM: A Library for Support Vector Machines, ACM Transactions on Intelligent Systems and Technology, 2(3), 2011, pp. 1-27.
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain the copyright and publishing right, and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) followingthe publication of the article, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).