Construction of Virtual Simulation Experiment Platform for Intelligent Construction Based on Statistical Machine Learning System Modelling

Abstract

In the construction of a virtual simulation experiment platform for intelligent construction, cutting-edge technologies converge to revolutionize traditional project management methodologies. By harnessing the power of virtual reality, statistical modeling, and machine learning, this platform empowers stakeholders to predict, optimize, and simulate construction projects with unprecedented accuracy and efficiency. This paper introduces the Virtual Statistical Machine Learning (VS-ML) platform and demonstrates its application in intelligent construction processes. Through comprehensive experimentation and simulation, the VS-ML platform accurately estimates construction project parameters, optimizes resource utilization, schedules tasks efficiently, and classifies project outcomes with high accuracy. Numerical results from our study showcase the platform's effectiveness in various aspects of construction project management. For instance, in construction projects estimation, scenarios ranging from Scenario 1 to Scenario 10 exhibit project durations between 100 to 150 days, cost estimates ranging from $470,000 to $550,000, and safety ratings varying from "Good" to "Excellent". Furthermore, labor efficiency and material waste estimations across scenarios demonstrate percentages ranging from 85% to 93% and 3% to 7%, respectively, with corresponding safety ratings. Additionally, task computations elucidate the durations, start dates, end dates, and resource allocations for individual tasks within construction projects. Lastly, classification results exhibit the predicted probabilities and class labels for samples, showcasing the platform's ability to accurately predict project outcomes. Overall, the findings underscore the potential of VS-ML in revolutionizing traditional construction practices through data-driven approaches, leading to improved project management, cost savings, and enhanced safety standards in the construction industry.