Mr. yassine bouabdallaoui
Centrale Lille, France
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Academic Career Length
7Years (2020-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
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Journal Article
yassine bouabdallaoui
Zoubeir LAFHAJPYPascal YIM
Pascal YIM
Laure DucoulombierBBBelkacem Bennadji
Belkacem BennadjiIn recent years, facility management (FM) has adopted many computer technology solutions for building maintenance, such as building information modelling (BIM) and computerized maintenance management systems (CMMS). However, maintenance requests management in buildings remains a manual and a time-consuming process that depends on human management. In this paper, a machine-learning algorithm based on natural language processing (NLP) is proposed to classify maintenance requests. This algorithm aims to assist the FM teams in managing day-to-day maintenance activities. A healthcare facility is addressed as a case study in this work. Ten-year maintenance records from the facility contributed to the design and development of the algorithm. Multiple NLP methods were used in this study, and the results reveal that the NLP model can classify work requests with an average accuracy of 78%. Furthermore, NLP methods have proven to be effective for managing unstructured text data.
About Me
Yassine BOUABDALLAOUI is a civil engineer who graduated from the Hassaniya School of Public Works (EHTP) in 2018 in Casablanca (Morocco). He also holds a master's degree from Ecole Centrale de Lille (France). Yassine has always been interested in numerics and mathematics. Integrating new approaches of Machine Learning and Data science in the construction industry is a challenge for him.
He started his thesis at the Ecole Centrale de Lille, in the Chair Construction 4.0, under the direction of Professor Zoubeir LAFHAJ in November 2018 . This thesis is under co-direction with Professor Pascal YIM from Ecole Centrale Lille.
This research project is part of the Artificial Intelligence theme. The research project aims to identify the potential of artificial intelligence and big data in the AEC industry, in order to propose some applications to enhance the value of data in the construction industry and improve productivity.
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