Abstract
Structural engineering is the engineering discipline that deals with a structure's or building's structural robustness. Structural engineering is a civil engineering specialisation that guarantees structures are secure, stable and do not collapse when loaded. The process of construction takes cost and time that has huge impact in the entire process of completing a certain project. Eminence of construction is assured with the optimized cost usage and minimal time consumption. Estimation or optimization of theses aspects is complicated, and it necessitated probabilistic approaches. Several researches developed cost estimation techniques, which are based on statistical and computational techniques. These approaches face certain issues namely optimization and occurrence of error during the estimation process. To handle this, artificial neural network (ANN) is developed for optimization of cost and time whereas the incidence of error is also minimal in the proposed approach. The main intent of the proposed approach is to enhance the construction procedures and methodologies using deep learning technique. ANN outperforms the existing state-of-the-art techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).