首页|New Findings on Machine Learning from Virginia Polytechnic Institute and State University (Virginia Tech) Summarized (Developing a Machine-learning Model To Predict Clash Resolution Options)
New Findings on Machine Learning from Virginia Polytechnic Institute and State University (Virginia Tech) Summarized (Developing a Machine-learning Model To Predict Clash Resolution Options)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Asce-Amer Soc Civil Engineers
Current study results on Machine Learning have been published. According to news reporting from Blacksburg, Virginia, by NewsRx journalists, research stated, “Even with the utilization of software tools like Navisworks to automate clash detection, clash resolution in construction projects remains a slow and manual process. The reason is the meticulous nature of the process where coordinators need to ensure that resolving one clash does not lead to new clashes.” 53 The news correspondents obtained a quote from the research from Virginia Polytechnic Institute and State University (Virginia Tech), “The use of machine learning to automate clash resolution as a potential option to improve the clash resolution process has been suggested with research showing positive results to support the implementation. While the research shows high accuracy in predicting clash resolution options to support automation, the scope limits the discussion on the complex and often lengthy process of developing a machine-learning model. Based on this research gap, the authors in this paper discuss the development of a prediction model to identify clash resolution options for given clashes. The discussion is focused on individual steps involved in creating machine-learning models like data collection, data preprocessing, and machine-learning algorithm development and selection. The authors also address common challenges in the development of machine-learning models including class imbalance and availability of limited data. The authors utilize a multilabel synthetic oversampling method to generate different percentages of synthetic data to account for class imbalance and limited data sets. Using this data set, the authors trained five machine-learning algorithms and reported on their accuracy.”
BlacksburgVirginiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningVirginia Polytechnic Institute and State University (Virginia Tech)
NETL
NSTL
Asce-Amer Soc Civil Engineers
