首页|New Machine Learning Research Reported from Faculty of Civil Engineering (Machin e Learning Model for Construction Time Prediction: A Case of Selected Public Bui lding Projects in Hosanna, Ethiopia)
New Machine Learning Research Reported from Faculty of Civil Engineering (Machin e Learning Model for Construction Time Prediction: A Case of Selected Public Bui lding Projects in Hosanna, Ethiopia)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting from the Faculty of Civil Engineer ing by NewsRx journalists, research stated, “The duration of a construction proj ect is a vital factor to consider before the commencement of the new project.” The news journalists obtained a quote from the research from Faculty of Civil En gineering: “Nowadays, the common problem in the construction industry is time ov errun. The main reason for this is the poor prediction of construction contract durations. Therefore, the objective of this study is to evaluate and validate Br omilow’s time-cost model and Love et al.’s time-floor model to estimate early pr oject durations for public building construction projects in the Hadiya Zone. Th e study also suggested an alternative duration machine learning prediction model by considering possibly influential project influencing factors. A questionnair e survey is designed to collect data, and subsequently, the study was performed using the Python programming language for development and validation purposes wi th different libraries used. The study developed Bromilow’s time-cost model usin g a simple linear regression algorithm and Love et al.’s time-floor model using a multiple linear regression algorithm and proposed a parametric model using ran dom forest, XGBoost, decision tree, K-nearest neighbor, and polynomial regressio n algorithms.” According to the news reporters, the research concluded: “This study extends the body of knowledge related to construction time performance, and it contributes valuable insights that inform the implementation of machine learning model for c onstruction time prediction.”
Faculty of Civil EngineeringCyborgsE merging TechnologiesMachine Learning
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