| dc.description.abstract | From the year 2018 to 2021, the construction projects in Taiwan have generated an annual average of over 36 million cubic meters of construction residual soil. This could lead to a surge in illegal cases of disposal and pose a severe threat to environmental hygiene and public safety. This research aims to explore whether the electronic joint consignment note system can effectively predict abnormal patterns in the flow of construction residual soil. Additionally, it investigates whether the use of the electronic joint consignment note as a new management model can enhance efficiency in management and reduce the manpower burden on construction residual soil management. The data collection targeted at the entire electronic joint consignment notes in the New Taipei City for the year of 2022, resulting in over 350,000 datasets. The experts suggested 7 variables as inputs out of the entire 14 variables, including Construction Registration Number, Joint Consignment Note Number, Quantity, Travel Time, and Abnormal Status, Exit Site, and Entry Site. Adopting 5 most popular classifiers for prediction including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Self-Organizing Map (SOM), and Tree-Structured Self-Organizing Map (TS-SOM), the study utilizes 5-fold cross validation to yield the results and performs comparison and analysis. The findings conclude that RF has the best prediction at 99.84%, suggesting practitioners with improvements of (1) more detailed travel time abnormal warning, (2) more input variables for the electronic joint consignment note system, and (3) user feedback mechanism. | en_US |