MSCA based Deep Recurrent Neural Network for Statistics Risk Management in Construction Projects

Abstract

Risk management plays a vital role in various construction activities to maximise construction profitability and reduce the loss of construction projects. Managing risk in construction projects is considered the most significant process in achieving the objectives in terms of quality, cost and time. Also, it is necessary to prioritise and identify the most probable risk that occurs during construction projects. Due to the unanticipated risk, around 40% of construction projects are dropdown in developing countries. This paper aims to develop and identify the project delay risk at a minimum duration of time and cost. Our paper comprises of five major phases: Identification of risk source and its factors, Systemization and Pre-processing of the dataset, Analysis of dataset constraints, Sensitivity data computation, and Tool selection using DRNN-MSCA to determine the risk, thereby establishing an effective and accurate prediction analysis. Here, the machine learning algorithm, namely the Deep Recurrent Neural Network (DRNN) and the Modified Sine Cosine Optimization Algorithm (MSCA), is integrated to minimise the inter-dependence and the complexity of the construction delay. The 5-point Likert scale computes the probability and the variables impact by the measures from very low to a very high level. Finally, the performance of the proposed approach is calculated and compared with a few other existing approaches such as ANN (Artificial Neural Network), RF-GA (Random forest and Genetic algorithm) and ML (Machine learning). The results reveal that the proposed approach provides a superior accuracy performance is 81.65%, with less cost and time delay.