Infrastructure Engineering - Research Publications

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    Requirements of a data storage infrastructure for effective land administration systems: case study of Victoria, Australia
    Shojaei, D ; Badiee, F ; Olfat, H ; Rajabifard, A ; Atazadeh, B (TAYLOR & FRANCIS LTD, 2022-01-27)
    Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions.
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    Understanding the Characteristics of Pedestrians when Passing Obstacles of Different Sizes: An Experimental Study
    Alhawsawi, AN ; Sarvi, M ; Felemban, E ; Rajabifard, A ; Wang, J (Forschungszentrum Julich, Zentralbibliothek, 2021-10-03)
    The aim of this study is to understand the collective movements of individuals and to observe how individuals interact within a physical environment in a crowd dynamic, which has drawn the attention of many researchers. We conducted an experimental study to observe interactions in the collective motions of people and to identify characteristics of pedestrians when passing obstacles of different sizes (bar-shaped, 1.2 m, 2.4 m, 3.6 m and 4.8 m), going through one narrow exit and employing three different flow rates in walking and running conditions. According to the results of our study, there were no differences in collision-avoidance behaviour of pedestrians when walking or running. The pedestrians reacted early to the obstacles and changed the direction in which they were walking by quickly turning to the left or to the right. In terms of the speed of the pedestrians, the average velocity was significantly affected while performing these tasks, decreasing as the size of the obstacle increased; therefore, the size of obstacles will affect flow and speed levels. Travel time was shorter when participants were in the medium-flow rate experiments. In terms of the distance of each individual’s travel, our data showed that there was no significant difference in all the flow rate experiments for both speed levels. Our results also show that when the pedestrians crossed an obstacle, the lateral distance averaged from 0.3 m to 0.7 m, depending on the flow rate and speed level. We then explored how the body sways behaved while avoiding obstacles. It is observed that the average sway of the body was less in the high-speed conditions compared to the low-speed conditions – except for the HF & 4.8 m experiment. These results are expected to provide an insight into the characteristics of the behaviour of pedestrians when avoiding objects, and this could help enhance agent-based models.
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    Building Information Modeling (BIM) for Construction and Demolition Waste Management in Australia: A Research Agenda
    Han, D ; Kalantari, M ; Rajabifard, A (MDPI, 2021-12-01)
    Construction and demolition waste (C&DW) contribute to approximately 30% of the total waste generation worldwide, by which heterogeneous ecological impacts, such as resource depletion, global warming, and land degradation, are engendered. Despite ongoing research efforts to minimize construction waste via the Building Information Modeling (BIM)-aided design, there is a paucity of research on integrating BIM in demolition waste management (DWM). This study investigates prominent barriers and future research directions toward the wider adoption of BIM in C&DWM by conducting a systematic literature review. First, this study identifies the barriers that hinder the implementation of C&DWM in Australia; then, it explores the benefits and challenges of leveraging BIM applications for C&DWM. The findings suggest that, for existing buildings without up-to-date design drawings, it is imperative to improve the accuracy of data capturing and object recognition techniques to overcome the bottlenecks of BIM-DWM integration. Moreover, the development of regional-oriented material banks and their harmonization with life cycle assessment databases can extend the potential of BIM-based sustainability analysis, making it applicable to the DWM domain. This study proposes a research agenda on tackling these challenges to realize BIM’s full potential in facilitating DWM.
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    Evaluating the role of partnerships in increasing the use of big Earth data to support the Sustainable Development Goals: an Australian perspective
    Mohamed-Ghouse, ZS ; Desha, C ; Rajabifard, A ; Blicavs, M ; Martin, G (TAYLOR & FRANCIS LTD, 2021-10-29)
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    INVERSE MODEL USING LAND AND PROPERTYSUB-SYSTEMS FOR PLANNING FUTURE CITIES: A GENERAL FRAMEWORK
    Crespo, R ; Rajabifard, A (CICADIT, University of Bucharest, 2022-01-01)
    This paper suggests, based on literature review, the use of the inverse model coupled with land and property systems to support urban decision-making. The inverse model is to be used for planning decisions today to achieve the desired tomorrow. This approach has been used previously in urban planning with a property system. The use of a property system alone is insufficient in dealing with the complexity of urban systems. Complex systems are made up of sub-systems that interact with each other; the integration of two sub-systems offers a first and simple alternative to address the complexity of urban systems. We suggest the use of two parametric approaches, logistic regression and house price, to model land and property sub-systems, respectively. Finally, we stress that further studies are needed to integrate the inverse model with other statistical techniques that also deal with complexity, such as cellular automata (CA) or agent-based models.
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    ROOM-BASED ENERGY DEMAND CLASSIFICATION of BIM DATA USING GRAPH SUPERVISED LEARNING
    Kiavarz, H ; Jadidi, M ; Rajabifard, A ; Sohn, G (Copernicus GmbH, 2021-10-07)
    Abstract. Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in Building Energy Consumption Estimation (BECE). Despite extensive research, BIM is less used in BECE data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.
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    Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia
    Sharma, SK ; Aryal, J ; Rajabifard, A (MDPI AG, 2022-04-01)
    The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts.
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    Evaluation of the International 3D Geospatial Data Models and IFC Standard for Implementing an LADM-based 3D Digital Cadastre
    Atazadeh, B ; Olfat, H ; Rajabifard, A ; Saeidian, B (International Federation of Surveyors (FIG), 2022)
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    Formative and Summative Validation of Building Information Model-Based Cadastral Data
    Asghari, A ; Kalantari, M ; Rajabifard, A (MDPI, 2021-08-01)
    Among 3D models, Building Information Models (BIM) can potentially support the integrated management of buildings’ physical and legal aspects in cadastres. However, there is not a systematic approach to author the cadastral information into the BIM models. Moreover, the common approaches for data validation only check the final cadastral output, and they ignore the data generation steps as potential avenues for validation. Therefore, this study aims to develop the criteria and standards to check the spatial consistency and integrity of BIM-based cadastral data in the process of generating the data. The paper utilises a case study approach as its methodology to investigate the requirements of generating a BIM-based cadastral model and identify the issues within the process. The results include a formative assessment (i.e., multistep validation approach during the data generation) alongside a summative assessment (i.e., one-step validation approach at the end of data generation). This study found the summative assessment alone insufficient for 3D cadastral data validation. The paper concludes that a formative and summative assessment together can improve the validity of the data. The results will potentially bring more efficiency to modern land administration processes by avoiding the accumulation of errors in 3D cadastral data generation.