Strength Durability-Based Design Mix of Self-Compacting Concrete with Cementitious Blend using Hybrid Neural Network-Genetic Algorithm

Sustainable development in self-compacting concrete (SCC) has been studied extensively for the recent years for the purpose to address its growing demand in construction projects. Sustainable SCC can be defined as concrete mix with partially replaced cement content that varies from low to high level using different mineral admixtures. Silica fume and fly ash which is considered as the most common sustainable mineral admixtures for binary and ternary cementitious blends show good effect to the compressive strength and chloride penetration resistivity of hardened SCC. In this paper, this effect was further investigated by using two widely used biological inspired computing models namely the artificial neural network (ANN) and genetic algorithm (GA). The test results of compressive strength and chloride ion penetration resistance from thirty-six concrete samples with varying replacement level of binary and ternary cementitious blends were utilized as inputs for model development. ANN was used to obtain models that describe analytically the relationship of material components to the compressive strength and chloride penetration resistivity. The derived models were further explored through optimization using GA. Results shows that ANN was able to establish the relationship of strength-durability parameters to the material components while GA is able to derived optimal mix proportion for best strength-durability performance. The present study also validates the sensitivity of the replacement level of silica fume and fly ash as a ternary cementitious blend to the strength-durability performance of SCC. This indicates that high volume content of ternary blended cement can improve chloride penetration resistivity and exhibited high compressive strength

Hybrid artificial intelligence-based bond strength model of CFRP-lightweight concrete composite

Different retrofitting techniques are commonly used to sustain the design life of heavy damage and deteriorated concrete structures, whilst epoxy-bonded carbon fiber reinforced polymer (CFRP) has emerged as a widely known retrofitting method. Consequently, a sound understanding of the bond strength between structural lightweight concrete (LWC) and CFRP based on influential factors is essential in safety and economic requirements. In this study, a hybrid bond strength model using the artificial neural network (ANN) and genetic algorithm (GA) was developed to furtherly understand the bond of a CFRP strengthened LWC structure. ANN was able to establish under satisfactory performance the relationship between the maximum bond load and the following influential parameters: width of CFRP (bfrp), total CFRP bond length (Lfrp), CFRP thickness (tfrp), and CFRP angle of orientation (θfrp). Furthermore, GA was able to derive the optimal configuration of the influential parameters resulted in high bond performance. Moreover, the optimization results also validated the sensitivity of each parameter on the interfacial bond behavior between LWC and CFRP