異なる養生条件下のセメント改良土の強度発現機構に関する化学・微細構造分析

内容の要約広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Long-term mechanical properties and durability of high-strength concrete containing high-volume local fly ash as a partial cement substitution

High-volume fly ash (HVFA) is a commonly used partial substitution for cement in mortar and concrete production. The present study was developed to examine the potential of using local HVFA as a partial substitution for cement in high-strength concrete (HSC). The effects of FA inclusion on the long-term mechanical properties and durability of concrete were investigated. The HSC samples were prepared with different FA substitution ratios, ranging from 0% to 50% by weight (ratio between FA to total weight of cement and FA) at 10% interval. Long-term properties were evaluated through compressive strength (CS) and ultrasonic pulse velocity (UPV) tests, while durability was examined through water absorption (WA), drying shrinkage (DS), and rapid chloride ion penetration (RCPT) tests. The results indicate that, at 28 and 56 days, higher FA content was associated with lower HSC performance. However, at 120 days, the 30% FA sample achieved the highest performance of all of the samples in terms of long-term mechanical properties and durability. Moreover, higher FA content was associated with less DS. Also, no significant change in the water absorptivity value of the HSC samples was observed. All of the HSC samples at 28, 56, and 120 days of curing age had UPV values of above 4100 m/s, indicating “very good quality” concrete. In addition, all of the HSC samples exhibited “very low” chloride permeability. Finally, the results of the environmental analysis found that incorporating a high volume of locally sourced FA as a partial substitution for cement in HSC reduced both CO2 emissions (CO2-E) and energy consumption (EC), which may help further promote the sustainability of the construction industry

The SHAP values derived from the GB model.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.</div

The magnitude of input variables and output.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.</div

Partial dependence plots (PDP-2D) analysis of the coupled input factors affecting the CS of the hollow concrete specimens.

Partial dependence plots (PDP-2D) analysis of the coupled input factors affecting the CS of the hollow concrete specimens.</p

The framework of the GB algorithm.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.</div

Values of R² values with different hyperparameter values of the GB model.

Values of R2 values with different hyperparameter values of the GB model.</p

Correlation matrix of the input and output variables.

Correlation matrix of the input and output variables.</p

The values of compressive strength in training and testing.

The values of compressive strength in training and testing.</p