Xue(on the right side) was very honoured to receive‘The Best Presentation Award’.
It was very exciting for Maxwell(on the rightside)to get ‘The Most PopularPresentation Award’.
Congratulations to our research students,Ms Xue Xiao and Mr. Antwi-Afari Maxwell Fordjour on winning the Best Presentation Award and the Most Popular Presentation Award, respectively in the recent ‘16thAcademic Conference for Postgraduate Students in Construction Management and Real Estate’!
In this Conference,a group of research students from the University of Hong Kong, the Hong Kong Polytechnic University and the Shenzhen University had an academic exchange of their studies in the area of Construction Management and Real Estate.Meanwhile, there was a very keen competition among the participating research students for the awards.
Under the supervision of Ir Prof. Heng Li, Chair Professor of Construction Informatics in PolyU, Xue’s study aims at developing a green building experience-mining (GBEM) model to adapt previous solutions to a new situation while the objective of Maxwell’s paper is to develop a method to detect and classify slip, trip, and loss of balance (STL) events based upon foot plantar pressure distribution data captured using wearable insole pressure sensors.Ticking all the right boxes, their hard work well deserved the accolades and the warmest congratulations.
More about Ms Xue XIAO’s Paper ‘Case-Based Reasoning and Text Mining for Green Building Decision Making’:
Title:
Case-Based Reasoning and Text Mining for Green Building Decision Making
Abstract:
Great benefits are obtained by sharing previous experiences in meeting the needs of the standard evaluation systems for green building around the world. To date, there are no existing methods available that enable this to take place in a systematic way. This paper addresses the issue by developing a green building experience-mining (GBEM) model that enables previous green building solutions to be adapted for a new situation.A database of 10 cases is used to demonstrate and evaluate the effectiveness of the GBEM model. The results confirm the model’s potential to facilitate users in the selection of the solutions when addressing new green building challenges.
Keywords:
Case-based reasoning, Green building Text mining
Name:Xiao Xue
Department:the Department of Building and Real Estate, PolyU
Email address:xue.xiao@connect.polyu.hk
More about Mr. ANTWI-AFARIMaxwell Fordjourand his paper ‘Wearable Insole Pressure Sensors for Automated Classification of Construction workers’ Slip-Trip-Loss of Balance Events’:
Title:
Wearable Insole Pressure Sensors for Automated Classification of Construction workers’ Slip-Trip-Loss of Balance Events
Abstract:
The objective of the current study was to develop a method to detect and classify slip, trip, and loss of balance (STL) events based upon foot plantar pressure distribution data captured using wearable insole pressure sensors. Twenty young healthy participants participated in experimental trials involving falls on the same level due to STL events experienced by construction workers. Foot plantar pressure distribution data acquired during the STL events were input to supervised machine learning classifiers[e.g., decision tree (DT), artificial neural network (ANN),K-nearest neighbor (KNN),and support vector machine (SVM)]. The results show good accuracy, sensitivity,and specificity of the classifiers, confirming the feasibility of detecting potential fall risks effectively. The implications of this study are of value to researchers and practitioners because the method quantitatively measures the type of events and provides a computational tool that records automated foot plantarpressure distributions, which can help understand fundamental causes off all-related injuries in construction workers.
Keywords:
Construction worker,Loss of balance, Pressure sensor, Slip, Trip.
Name:Antwi-Afari Maxwell Fordjour
Department:the Department of Building and Real Estate, PolyU
Email address:maxwell.antwiafari@connect.polyu.hk
