首页|New Machine Learning Study Findings Have Been Reported by Investigators at Depar tment of Architectural Engineering (Construction of Virtual Simulation Experimen t Platform for Intelligent Construction Based On Statistical Machine Learning Sy stem ...)
New Machine Learning Study Findings Have Been Reported by Investigators at Depar tment of Architectural Engineering (Construction of Virtual Simulation Experimen t Platform for Intelligent Construction Based On Statistical Machine Learning Sy stem ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Investigators publish new report on Ma chine Learning. According to news reportingoriginating from Hebei, People's Rep ublic of China, by NewsRx editors, the research stated, "In the constructionof a virtual simulation experiment platform for intelligent construction, cutting-e dge technologiesconverge to revolutionize traditional project management method ologies. By harnessing the power of virtualreality, statistical modeling, and m achine learning, this platform empowers stakeholders to predict,optimize, and s imulate construction projects with unprecedented accuracy and efficiency."Our news editors obtained a quote from the research from the Department of Archi tectural Engineering,"This paper introduces the Virtual Statistical Machine Lea rning (VS-ML) platform and demonstrates itsapplication in intelligent construct ion processes. Through comprehensive experimentation and simulation,the VS-ML p latform accurately estimates construction project parameters, optimizes resource utilization,schedules tasks efficiently, and classifies project outcomes with high accuracy. Numerical results from ourstudy showcase the platform's effectiv eness in various aspects of construction project management. Forinstance, in co nstruction projects estimation, scenarios ranging from Scenario 1 to Scenario 10 exhibitproject durations between 100 to 150 days, cost estimates ranging from $470,000 to $550,000, and safetyratings varying from ‘Good' to ‘Excellent'. Furthermore, labor efficiency and material waste estimati onsacross scenarios demonstrate percentages ranging from 85% to 9 3% and 3% to 7%, respectively, with corresponding safety ratings. Additionally, task computations elucidate the duratio ns, start dates, end dates,and resource allocations for individual tasks within construction projects. Lastly, classification results exhibitthe predicted pro babilities and class labels for samples, showcasing the platform's ability to ac curatelypredict project outcomes."
HebeiPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningDepartment of Architectural E ngineering
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