Vincent J.L. Gan

Bio: Vincent J.L. Gan is an academic researcher from National University of Singapore. The author has contributed to research in topics: Building information modeling & Computer science. The author has an hindex of 17, co-authored 42 publications receiving 764 citations. Previous affiliations of Vincent J.L. Gan include Hong Kong University of Science and Technology.

Integrating 4D BIM and GIS for Construction Supply Chain Management

Abstract: Construction supply chain management (CSCM) requires the tracking of material logistics and construction activities, an integrated platform, and certain coordination mechanisms among CSCM p...

Flood susceptibility modelling using advanced ensemble machine learning models

Abstract: Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

A Clustering Regression Approach: A Comprehensive Injury Severity Analysis of Pedestrian-Vehicle Crashes in New York, U.S. and Montreal, Canada

Abstract: Understanding the underlying relationship between pedestrian injury severity outcomes and factors leading to more severe injuries is very important in dealing with the problem of pedestrian safety. To investigate injury severity outcomes, many previous works relied on statistical regression models. There has also been some interest for data mining techniques, in particular for clustering techniques which segment the data into more homogeneous subsets. This research combines these two approaches (data mining and statistical regression methods) to identify the main contributing factors associated with the levels of pedestrian injury severity outcomes. This work relies on the analysis of two unique pedestrian injury severity datasets from the City of New York, US (2002-2006) and the City of Montreal, Canada (2003-2006). General injury severity models were estimated for the whole datasets and for sub-populations obtained through clustering analysis. This paper shows how the segmentation of the accident datasets help to better understand the complex relationship between the injury severity outcomes and the contributing geometric, built environment and socio-demographic factors. While using the same methodology for the two datasets, different techniques were tested. For instance, for New York, latent class with ordered probit method provides the best results. However, for Montreal, the K-means with multinomial logit model is identified as the most appropriate technique. The results show the power of using clustering with regression to provide a complementary and more detailed analysis. Among other results, it was found that pedestrian age, location at intersection, actions prior to accident, driver age, vehicle type, vehicle movement, driver alcohol involvement and lighting conditions have an influence on the likelihood of a fatal crash. Moreover, several features within the built environment are shown to have an effect. Finally, the research provides recommendations for policy makers, traffic engineers, and law enforcement to reduce the severity of pedestrian-vehicle collisions.

Incorporation of phase change materials into building envelope for thermal comfort and energy saving: A comprehensive analysis

Abstract: Building envelope is a key element in providing adequate energy and thermal comfort performance to buildings. In this regard, improvement solutions are implemented in recent studies that focus on new techniques and methods. The main techniques adopted in this context are discussed to identify modern and effective methods with a particular focus on phase change materials (PCMs). Incorporating PCMs with building construction materials is a booming technology, owing to their enhancement potential of storing and releasing heat during phase transition. This work highlights the importance of PCMs in building envelope, focusing on roof and external wall applications. PCM types, general and desired properties and application area are presented and discussed. Influential parameters, incorporation techniques and methods, main numerical tools, and modelling equations are used to describe the thermal behaviour of PCM. A comprehensive assessment on the basis of recent studies has been conducted to point out the potential of PCM with the most appropriate techniques under different locations. The main findings of PCM thermal performance have been described, considering the cooling/heating load reduction, energy-saving and thermal comfort gained along with several research hiatuses for future studies.

Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review

Abstract: The purpose of this paper is to provide an overview of the current state of research and the key aspects and implications of the relationships between Information and Digital Technologies (IDT) of ...