Application of support vector machine-particle swarm optimization-genetic algorithm coupled with building information modeling to predict advanced construction problems

The objective of this study is to explore the usage of artificial algorithms in developing information modeling and analyze the reactions of the participants. It should be note that all of the statistical society in this project was 56 persons. A diverse range of artificial algorithms should be chosen for this application. For this matter, the models chosen for this study are those that have shown the highest level of success. There is a pressing need to verify the proper functioning of the patterns implemented in the computer program, as well as the accurate response and modeling of the inputs and the evaluation of the optimization logic of the existing relationships in computing. This requires conducting a parallel study and presenting a validated and reliable research. The task is completed in accordance with the reported instances. The support vector machine-particle swarm optimization-genetic algorithm (SVM-PSO-GA), an artificial method, is used to forecast outcomes based on experimental data sets. In this study, the experimental dataset is analyzed using statistical techniques and a systematic approach. The raw data is processed through a step-by-step process that involves summarizing, coding, categorizing, and assessing validity and reliability using the KMO index, Bartlett's test, and Cronbach's alpha test. The study focuses on developing a model using CIM (Civil Information Modeling) and BIM (building information modeling) to identify and analyze the key aspects that contribute to cost reduction in metro tunnel projects. Subsequently, a comprehensive analysis of the financial optimization of urban trains is offered, focusing on the use of Building Information Management. This approach provides a means to develop urban transportation infrastructures that are both economically robust and sustainable.