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Optimization of mixture proportions in ternary low-heat Portland cement-based cementitious systems with mortar blends based on projection pursuit regression
Highlights PPR-based model of mechanical and thermal properties is established. Optimum mixture proportion is acquired based on PPR predicting models. PPR-based optimization method was a promising engineering tools for prediction.
Abstract The low hydration heat of low-heat Portland (LHP) cement-based cementitious systems affects their mechanical properties at an early age. Models for predicting mechanical and thermal performances and optimizing the mixture proportion in ternary LHP cement-based cementitious systems with mortar blends will be useful. In this study, the projection pursuit regression (PPR) model, which is a nonassuming modeling technique, is applied to predict the relationship between the mixture proportion and compressive strength and hydration heat of LHP cement-based cementitious systems with mortar. The optimum mixture proportion was determined via analysis of the contour plots compressive strength and hydration heat as functions of fly ash and ground-granulated blast-furnace slag content in accordance with the demand of actual mass concrete engineering. The PPR model was compared with other methods and found to present higher calculation accuracy. The stability and modeling efficiency of the PPR model were determined and verified by proposing the “accuracy consistency test” criterion and modeling sample selection criteria. The multiobjective optimization problem was converted into two single-objective optimization problems by the PPR model and the optimization method. The problems of artificial parameter assignment and assumptions, which exist in traditional multiobjective optimization, were thus avoided. The PPR model is a valuable tool for predicting the properties of cementitious systems and optimizing the mixture proportion in cementitious systems.
Optimization of mixture proportions in ternary low-heat Portland cement-based cementitious systems with mortar blends based on projection pursuit regression
Highlights PPR-based model of mechanical and thermal properties is established. Optimum mixture proportion is acquired based on PPR predicting models. PPR-based optimization method was a promising engineering tools for prediction.
Abstract The low hydration heat of low-heat Portland (LHP) cement-based cementitious systems affects their mechanical properties at an early age. Models for predicting mechanical and thermal performances and optimizing the mixture proportion in ternary LHP cement-based cementitious systems with mortar blends will be useful. In this study, the projection pursuit regression (PPR) model, which is a nonassuming modeling technique, is applied to predict the relationship between the mixture proportion and compressive strength and hydration heat of LHP cement-based cementitious systems with mortar. The optimum mixture proportion was determined via analysis of the contour plots compressive strength and hydration heat as functions of fly ash and ground-granulated blast-furnace slag content in accordance with the demand of actual mass concrete engineering. The PPR model was compared with other methods and found to present higher calculation accuracy. The stability and modeling efficiency of the PPR model were determined and verified by proposing the “accuracy consistency test” criterion and modeling sample selection criteria. The multiobjective optimization problem was converted into two single-objective optimization problems by the PPR model and the optimization method. The problems of artificial parameter assignment and assumptions, which exist in traditional multiobjective optimization, were thus avoided. The PPR model is a valuable tool for predicting the properties of cementitious systems and optimizing the mixture proportion in cementitious systems.
Optimization of mixture proportions in ternary low-heat Portland cement-based cementitious systems with mortar blends based on projection pursuit regression
Gong, Jingwei (author) / Jiang, Chunmeng (author) / Tang, Xinjun (author) / Zheng, Zuguo (author) / Yang, Lixing (author)
2019-11-19
Article (Journal)
Electronic Resource
English
Optimization of ternary cementitious mortar blends using factorial experimental plans
| Springer Verlag | 2002
Optimization of ternary cementitious mortar blends using factorial experimental plans
| Online Contents | 2002
Optimization of ternary cementitious mortar blends using factorial experimental plans
| Online Contents | 2002
Optimization of ternary cementitious mortar blends using factorial experimental plans
| British Library Online Contents | 2002