Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

Prayogo, Doddy; Cheng, Min‐Yuan; Widjaja, J. A.; Ongkowijoyo, Hansel; Prayogo, Handy

doi:10.22260/isarc2017/0120

Cited by 7 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LS-SVM was first developed by [8] as an improved version of the support vector machine (SVM). As a data mining technique, LS-SVM has been successfully applied in many civil engineering-related problems [14][15][16][17]. LS-SVM utilizes a cost function based on the least squares principle as opposed to the quadratic loss function that had been used in the original SVM [18].…”

Section: Regression Model: Ls-svmmentioning

confidence: 99%

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Prayogo

Susanto

2019

MATEC Web Conf.

Self Cite

View full text Add to dashboard Cite

Pile foundations usually are used when the upper soil layers are soft clay and, hence, unable to support the structures’ loads. Piles are needed to carry these loads deep into the hard soil layer. Therefore, the safety and stability of pile-supported structures depends on the behavior of the piles. Additionally, an accurate prediction of the piles’ behavior is very important to ensure satisfactory performance of the structures. Although many methods in the literature estimate the settlement of the piles both theoretically and experimentally, methods for comprehensively predicting the load-settlement of piles are very limited. This study develops a new data mining approach called self-learning support vector machine (SL-SVM) to predict the load-settlement behavior of single piles. SL-SVM performance is investigated using 446 training data points and 53 test data points of cone penetration test (CPT) data obtained from the previous literature. The actual prediction accuracy is then compared to other prediction methods using three statistical measurements, including mean absolute error (MAE), coefficient of correlation (R), and root mean square error (RMSE). The obtained results show that SL-SVM achieves better accuracy than does LS-SVM and BPNN. This confirms the capability of the proposed data mining method to model the accurate load-settlement behavior of single piles through CPT data. The paper proposes beneficial insights for geotechnical engineers involved in estimating pile behavior.

show abstract

Section: Regression Model: Ls-svmmentioning

confidence: 99%

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Prayogo

Susanto

2019

MATEC Web Conf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Research has shown that there is an increase in prediction accuracy when metaheuristic algorithm is used as the optimizer for model selection and has improved prediction performance in various engineering problems [2,6,9,10]. Many studies have shown that the SOS algorithm is better when compared to other metaheuristic algorithms in finding the optimal solutions to problems involving complex and nonlinear optimization [2,11].…”

Section: Model Selection In the Training Phasementioning

confidence: 99%

Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results

Prayogo¹

2018

ced

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…As we have argued, diverse efforts concerning the accuracy improvement of conventional approaches illustrate the importance of this task in civil engineering. On the other hand, benefiting from optimization strategies (e.g., grey wolf optimizer [46], symbiotic organism searches [47], teaching learning-based optimization [48], etc.) has been regarded an effective idea for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization

Akbarzadeh,

Ghafourian,

Anvari

et al. 2023

Materials

View full text Add to dashboard Cite

Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.

show abstract

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

Cited by 7 publications

References 24 publications

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Optimizing the prediction accuracy of load-settlement behavior of single pile using a self-learning data mining approach

Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization

Contact Info

Product

Resources

About