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

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 Preprocessing 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.