Cost Monitoring and Management Algorithms in the Construction Process of Building Projects

Abstract

Construction project cost estimation is an essential activity in construction-based fields; it offers a valuable source for the project feasibility studies and the design plan comparisons, and the accuracy can directly influence project investment decisions. A successful implementation of project cost forecasts makes it easier to control and manage project costs. Therefore, this paper presents the Ladybug Reptile Search Algorithm_ Deep Maxout Network (LRSA_DMN) (LRSA_DMN) to estimate construction costs. Additionally, LRSA is included to correct DMN weights. Furthermore, Bootstrap scheme is used to augment data and use this data to input the DMN for cost estimation. In addition, features are selected using a Weighted Correlation Coefficient to choose the best features for further processing. The test results show that LRSA_DMN performs better regarding MSE, RMSE, and Accuracy by 0.092, 0.371, and 0.907, respectively