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Meng-Han Tsai,Cheng-Hsuan Yang,James Yichu Chen,Shih-Chung Kang 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.2
This research proposes a four-stage consultant framework for applying a chatbot as a data management system. With the advancement of computational power and data storage technology, the increasing amount of data makes the issue of data management difficult to address. Management of a massive amount of data by utilizing chatbots to play the roles of a data manager and a data provider has been extensively studied. Although a chatbot system has been proven to increase the overall efficiency of data management, implementing a chatbot system in a government department remains a challenge, especially in a field with highly complex data. This research presents the authors’ experience of applying a chatbot system in a department of the government of Taiwan for disaster response operations. A four-stage consulting framework comprising 1) existing workflow review, 2) usability evaluation, 3) system improvement, and 4) management plan (EUSM) was thus proposed. After a two-year field test, the authors found that the framework could help the department in clarifying their working process, increase the overall efficiency of the chatbot system, and identify the major issues of introducing the chatbot system.
SEMA: A Site Equipment Management Assistant for Construction Management
Meng-Han Tsai,Cheng-Hsuan Yang,Chen-Hsuan Wang,I-Tung Yang,Shih-Chung Kang 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.3
Collecting construction equipment information such as the site equipment enter and exit date-time, driver's name, type, and quantity is essential in construction management. Most construction projects use paper to record the equipment access history. However, manual recording is always labour-intensive and time-consuming. Therefore, this research aims to develop an assistant system, Site Equipment Management Assistant (SEMA), to automate the site equipment management processes. With the introduction of image recognition and multiple objects tracking technologies, the proposed system can extract equipment-related information from raw videos. SEMA is designed as a chatbot system that contains three major modules: data acquisition, information extraction, and information delivery. A deep learning-based model was first trained to automatically recognize and track construction equipment passing by the site monitor. Information such as equipment entering and exiting date-time, type, and quantity would be extracted and stored in a database. A chatbot interface was developed for users to obtain data from the database through an intuitive and easy-to-use interface. A system evaluation and usability test were conducted. The results showed that the system could effectively improve the construction equipment management process. SEMA can save 60.7% of users' operation time on obtaining related information.