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Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing the trade-offs among multifaceted criteria. This study develops a BIM-based DWM sustainability assessment approach to facilitate the life cycle sustainability assessment (LCSA) and decision-making by integrating LCSA-related properties and hybrid Multi-Criteria Decision-Aiding (MCDA) methods into a BIM environment using Dynamo visual scripting. A dynamic linkage is developed in the streamlined BIM-based LCSA process, where the enriched Industry Foundation Class (IFC) models are coupled with custom LCSA data templates to achieve seamless data exchange between the BIM platform and external LCA tools. Subsequently, hybrid MCDA methods convert the assessment results into DWM scenario ranking. A pilot study verifies the applicability of the BIM-based framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the 'status quo' in China's recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the 'growth curve' of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the "Australian standard" scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, implying that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined settings. [ABSTRACT FROM AUTHOR]

Purpose: Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices. Design/methodology/approach: This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022. Findings: Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision. Originality/value: This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations. [ABSTRACT FROM AUTHOR]

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. [ABSTRACT FROM AUTHOR]

Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert's opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation. [ABSTRACT FROM AUTHOR]

Legal boundaries are used for delineating the spatial extent of ownership property's spaces. In underground environments, these boundaries are defined by referencing physical objects, surveying measurements, or projections. However, there is a gap in connecting and managing these boundaries and underground legal spaces, due to a lack of data model. A 3D data model supporting underground land administration (ULA) should define and model these boundaries and the relationships between them and underground ownership spaces. Prominent 3D data models can be enriched to model underground legal boundaries. This research aims to propose a new taxonomy of underground legal boundaries and model them by extending CityGML, which is a widely used 3D data model in the geospatial science domain. We developed, implemented, and tested the model for different types of underground legal boundaries. The implemented prototype showcased the potential benefits of CityGML for managing underground legal boundaries in 3D. The proposed 3D underground model can be used to address current challenges associated with communicating and managing legal boundaries in underground environments. While this data model was specifically developed for Victoria, Australia, the proposed model and approach can be used and replicated in other jurisdictions by adjusting the data requirements for underground legal boundaries. [ABSTRACT FROM AUTHOR]

Cadastral databases have been used for over 20 years, but most contain 2D data. The increasing presence of high-rise buildings with modern architecture complicates the process of determining property rights, restrictions, and responsibilities. It is, therefore, necessary to develop an efficient system for storing and managing multidimensional cadastral data. While there have been attempts to develop 3D cadastral database schemas, a comprehensive solution that meets all the requirements for effective data storage, manipulation, and retrieval has not yet been presented. This study aims to analyse the literature on 3D cadastral databases to identify approaches and technologies for storing and managing these data. Based on a systematic literature review integrated with a snowballing methodology, 108 documents were identified. During the analysis of the related documents, different parameters were extracted, including the conceptual data model, query type, and evaluation metrics, as well as the database management system (DBMS) used and technologies for visualisation, data preparation, data transformation, and the ETL (extract, transform, and load) process. The study emphasised the importance of adhering to database design principles and identified challenges associated with conceptual design, DBMS selection, logical design, and physical design. The study results provide insights for selecting the appropriate standards, technologies, and DBMSs for designing a 3D cadastral database system. [ABSTRACT FROM AUTHOR]

This paper explores the integration of Urban Digital Twin (UDT) technology in Melbourne's Greenline Project, focusing on a performance-based framework that aligns with Sustainable Development Goals and city-specific sustainability objectives. Recognising the complexities of urban ecosystems while anchoring on human-centric 'placemaking' – a participatory process involving the planning, design, and management of public spaces – the key capabilities and challenges in developing, implementing and applying UDTs and related technologies to achieve these objectives are reviewed and synthesised. Challenges in data collection and/or access, their use and integration into decision-making processes and capturing the dynamic interaction between physical and virtual environments, including updating UDT models are highlighted. The review emphasises the role of urban planners and stakeholders in driving UDT development and applications by better understanding and stating objectives, leveraging interdisciplinary collaboration for effective technology application, crucial for robust data management, analytics, and community contribution. International examples illustrate the potential utility of Digital Twins in enhancing urban planning, design, and operational management. The paper presents a preliminary framework to guide the further development of the Greenline Project and inform future research that is needed for improved and effective deployment and use of UDTs in other urban revitalisation projects. Practitioner pointers Urban planners have a central role in directing Urban Digital Twin technologies to address socio-economic and sustainability challenges facing cities and communities. Interdisciplinary collaboration, skills development, user-friendly technologies and sharing of real-time spatial data will drive innovation and support sustainable urban development. Developing performance frameworks will enhance planning and design practice, project monitoring and management and communications related to sustainable urban regeneration. [ABSTRACT FROM AUTHOR]

Addressing the sustainability issues arising from construction and demolition waste management (DWM) has gained little traction due to the lack of incentives, stringent regulations, and systematic guidance. This study aims to empower systematic decision-making concerning DWM alternative selection by developing a sustainability assessment framework by coupling a modified Delphi method with the multicriteria decision analysis technique. First, the study identifies a comprehensive inventory of indicators across three dimensions of sustainability in the context of DWM. Next, the study combines a modified Delphi method with the analytic hierarchy process to validate and prioritize the selected sustainability indicators. For the first time, insights regarding the DWM sustainability indicators from China's construction industry practitioners' perspectives are elicited using a mixed method comprising online semistructured interviews and two rounds of questionnaire surveys. Experts participating in the research are mostly based in Guangzhou and Shenzhen, where local governments exhaust all efforts in promoting carbon-neutral and sustainable development. The findings reveal that eight sustainability indicators were regarded as the determinants for the sustainability performance of DWM, with the global warming potential (32%), energy efficiency (16.1%) and land use (13.5%) receiving the highest preference scores (weights) based on the experts' judgment. Notably, the economic factors like the total cost (6.54%) appeared not highly prioritized by the local experts as typically did in the previous studies from developing countries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accelerating challenges to cities and communities have triggered a growing interest in the assessment of resilience and sustainability of future developments. For this purpose, many countries have adopted the United Nations' 2030 Agenda for sustainable development goals (SDGs), in which resilience has been incorporated as a component of sustainability. However, the framework has been criticised for not undertaking a comprehensive evaluation of resilience. This study, in an analytical scheme, examines the extent to which the SDGs incorporate measurement of resilience. Here, the SDGs indicators have been compared with the most recent comprehensive baseline resilience framework (BRF) through three stages: (a) thematic coding of the SDGs and BRF indicators; (b) developing matrices of coding for each resilience dimension; and (c) evaluating resilience measurement in terms of coverage by the SDGs. Results showed that although the SDGs indicators have a high level of coverage for resilience measurement through all 17 goals, some aspects are nonetheless overlooked. In this study, by categorising the goals into five groups based on their coverage of each resilience dimension, a guideline is created, demonstrating the goals relevant to each resilience dimension. Furthermore, a systematic framework of resilience indicators is also proposed to integrate the overlooked aspects of resilience into the SDGs and the post-2030 agenda. The advanced SDGs can serve as a joint framework to measure resilience and sustainability. [ABSTRACT FROM AUTHOR]

Underground Land Administration (ULA) plays a paramount role in recording, registering and managing underground ownership boundaries and rights, restrictions and responsibilities associated with underground assets. 3D digital models provide a great potential to modernise ULA as it is evident in research studies. Several steps, from data acquisition to the use of underground land data have been considered by studies to support 3D ULA. These steps form the ULA data lifecycle. This paper provides an overview of methods, techniques and tools used in different steps of the ULA data lifecycle and identifies research gaps, challenges, and potential opportunities for future studies. [ABSTRACT FROM AUTHOR]

In underground environments, survey elements such as survey points and observations provide the information required to define legal boundaries. These elements are also used to connect underground legal spaces to a geodetic survey network. Due to the issues of current 2D approaches for managing underground cadastral data, prominent 3D data models have been extended to support underground land administration. However, previous studies mostly focused on defining underground legal spaces and boundaries, with less emphasis on survey elements. This research aims to extend CityGML to support underground cadastral survey data. The proposed extension is based on the survey elements elicited from underground cadastral plans, which is then implemented for an underground case study area in Melbourne, Australia. This extension integrates underground survey data with legal and physical data in a 3D digital environment and provides an improved representation of survey elements, facilitating the management and communication of underground cadastral survey data. [ABSTRACT FROM AUTHOR]

Transformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people's experiences and knowledge regarding flooding disasters and highlighted communities' sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives. [ABSTRACT FROM AUTHOR]

Background: Fire behaviour simulation and prediction play a key role in supporting wildfire management and suppression activities. Aims: Using machine-learning methods, the aim of this study was to predict the onset of fire propagation (go vs no-go) and type of fire behaviour (surface vs crown fire) in southern Australian semiarid shrublands. Methods: Several machine-learning (ML) approaches were tested, including Support Vector Machine, Multinomial Naive Bayes and Multilayered Neural Networks, as was the use of augmented datasets developed with Generative Adversarial Networks (GAN) in classification of fire type. Key results: Support Vector Machine was determined as the optimum machine learning classifier based on model overall accuracy against an independent evaluation dataset. This classifier correctly predicted fire spread sustainability and active crown fire propagation in 70 and 79% of the cases, respectively. The application of synthetically generated datasets in the Support Vector Machine model fitting process resulted in an improvement of model accuracy by 20% for the fire sustainability classification and 4% for the crown fire occurrence. Conclusions: The selected ML modelling approach was shown to produce better results than logistic regression models when tested on independent datasets. Implications: Artificial intelligence frameworks have a role in the development of predictive models of fire behaviour. Models that predict wildfire behaviour in operational settings can support decision making across various fire management areas. Machine learning has the potential to develop predictive models of shrubland fire behaviour. This article tested the suitability of machine learning-based methods, which produced better results than logistic regression models. [ABSTRACT FROM AUTHOR]

The 3D information in Building Information Modeling (BIM) has received significant interest for smart city applications. Recently, employing Industry Foundation Classes (IFC) for BIM in data-driven methods for Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. However, despite extensive studies on applying the IFC data in BECE analysis, employing the full potential of the BIM remains poor due to its complex data model and incompatibility with data-driven algorithms. This paper proposes a framework to extract accurate semantic, geometry, and topology information from the room-level (space) IFC schema by introducing new geo-computation algorithms to deal with these challenges. Additionally, we define a new topological weighted relationship between spaces in different stories by combining common geometry area with energy resistance value. Eventually, the proposed weighted space-based graph will be constructed to decrease the original complexity of the IFC model, and it is compatible with graph-based machine learning algorithms. The results are promising, with more than 90% accuracy in extracting the geometry information for the convex and non-convex polyhedron rooms and 100% accuracy in detecting vertical and horizontal adjacent rooms. This study confirms the proposed approach's efficiency, accuracy, and feasibility for space-based BECE analysis. [ABSTRACT FROM AUTHOR]

3D geospatial data are being progressively adopted in urban land administration to represent 3D ownership rights in multistorey buildings. The integrity of urban land administration highly depends on the validity and quality of cadastral data. However, a large portion of research deals with the conceptual principles of internal and external spatial consistencies of 3D cadastral data. This article integrates the principles in practice and develops methods to check the validity of 3D parcels and their relationships in complex ownership settings. To evaluate the methods, this research adopts a case study using an 18-floor multistorey building with 248 lots and three types of common properties. The ownership rights of this building are delineated as polyhedral surface models, including non-2-manifold and non-simple polyhedra, and evaluated by implemented principles. The results reveal that the integrated methods can identify 3D cadastral errors, overlaps and gaps. The developed integrated approach can significantly advance urban land administration, as it facilitates complex 3D cadastral data validation. [ABSTRACT FROM AUTHOR]

Machinery operations on construction sites result in many serious injuries and fatalities. Practical training in a virtual environment is the key to improving the safety performance of machinery operators on construction sites. However, there is limited research focusing on factors responsible for the efficiency of virtual training in increasing hazard identification ability among novice trainees. This study analyzes the efficiency of virtual safety training with head-mounted VR displays against flat screen displays among novice operators. A cohort of tower crane operation trainees was subjected to multiple simulations in a virtual towards this aim. During the simulations, feedback was collected using a joystick to record the accuracy of hazard identification while a post-simulation questionnaire was used to collect responses regarding factors responsible for effective virtual training. Questionnaire responses were analyzed using interval type-2 fuzzy analytical hierarchical process to interpret the effect of display types on training efficiency while joystick response times were statistically analyzed to understand the effect of display types on the accuracy of identification across different types of safety hazards. It was observed that VR headsets increase the efficiency of virtual safety training by providing greater immersion, realism and depth perception while increasing the accuracy of hazard identification for critical hazards such as electric cables. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) seek to address complex global challenges and cover aspects of social development, environmental protection, and economic growth. However, the holistic and complicated nature of the goals has made their attainment difficult. Achieving all goals by 2030 given countries' limited budgets with the economic and social disruption that the COVID-19 pandemic has caused is over-optimistic. To have the most profound impact on the SDGs achievement, prioritising and improving co-beneficial goals is an effective solution. This study confirms that countries' geographic location and income level have a significant relationship with overall SDGs achievement. This article applies the Gradient Boosting Machine (GBM) algorithm to identify the top five SDGs that drive the overall SDG score. The results show that the influential SDGs vary for countries with a specific income level located in different regions. In Europe and Central Asia, SDG10 is among the most influential goals for high-income countries, SDG9 for upper-middle-income, SDG3 in low and lower-middle-income countries of Sub-Saharan Africa, and SDG5 in Latin America and the Caribbean upper-middle-income countries. This systematic and exploratory data-driven study generates new insights that confirm the uniqueness, and non-linearity of the relationship between goals and overall SDGs achievement. [ABSTRACT FROM AUTHOR]

This special issue addressed several urban phenomena by leveraging remote sensing data, technologies, and analytics. The increasing trend of urbanization has challenged the traditional ways of urban planning, design, and management. In this special issue, the remote sensing data and analytics are used to develop a flood risk assessment for subway areas [[15]] and to determine the nearest evacuation place for people and equipment from a disaster area [[16]]. Planning support systems [[5]], urban analytics data infrastructures [[6]], and multi-dimensional analytics [[7]] always supported the planning and design processes toward achieving livability, sustainability, and prosperity in cities. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

This paper suggests 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. [ABSTRACT FROM AUTHOR]

The development and use of underground space is a necessity for most cities in response to rapid urbanisation. Effective underground land administration is critical for sustainable urban development. From a land administration perspective, the ownership extent of underground assets is essential for planning and managing underground areas. In some jurisdictions, physical structures (e.g., walls, ceilings, and utilities) are also necessary to delineate the ownership extent of underground assets. The current practice of underground land administration focuses on the ownership of underground space and mostly relies on 2D survey plans. This inefficient and fragmented 2D-based underground data management and communication results in several issues including boundary disputes, underground strikes, delays and disruptions in projects, economic losses, and urban planning issues. This study provides a review of underground land administration from three common aspects: legal, institutional, and technical. A range of important challenges have been identified based on the current research and practice. To address these challenges, the authors of this study propose a new framework for 3D underground land administration. The proposed framework outlines the future research directions to upgrade underground land administration using integrated 3D digital approaches. [ABSTRACT FROM AUTHOR]

Cadastral surveying plays an important role in defining legal boundaries of land and property. The current practice for recording cadastral survey data mainly relies on 2D digital or analog documents. This practice is efficient for simple land parcels but can be challenged in complex building developments. To address the issues stemmed from 2D methods of representing cadastral survey data, 3D spatial information models can be considered as a viable solution for managing cadastral survey data. Building Information Modeling (BIM) enables colsslaborative 3D management of the design, construction, and operation of buildings. There have been extensive studies conducted to investigate the connectivity between BIM and 3D cadaster. Most of these studies focus on managing legal information, such as ownership boundaries and attributes, in BIM-based environments. However, there is limited investigation on how surveying measurements can be mapped into BIM. In this study, the proposed method for integrating the cadastral survey data into the BIM environment includes identifying cadastral survey requirements, using BIM entities relevant to cadastral survey data, enrichment of a BIM prototype, and evaluation of the prototype. The major contribution of this study is to demonstrate the storage of cadastral survey data such as survey marks and traverse lines in the BIM environment. Therefore, this research contributes to the further enrichment of BIM with incorporating data elements related to cadastral surveying practices. It is confirmed that current BIM-based tools provide restricted capabilities for explicit management and visualization of cadastral survey data. This limitation can be addressed in the future enhancements of BIM in terms of supporting important elements for cadastral survey data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: The achievement of the sustainable development goals (SDGs) for all communities and jurisdictions require a comprehensive roadmap that encompasses all dimensions of data infrastructure, social, economic, environmental and governance ecosystems. With this in mind, this paper aims to establish the link between the curriculum and intended learning outcomes of undergraduate and postgraduate subjects offered by the University and sustainability. This study is a part of a wider university strategy to embed sustainability knowledge and values in the university curricula. The 17 SDGs developed as a part of the 2030 Agenda for Sustainability Development was used as tool to measure and map how the subjects are linked with sustainability. Design/methodology/approach: To incorporate sustainability into the curriculum, this paper developed an interdisciplinary approach for analysing the interconnection between the SDGs, the expected subject learning points and the relevant aspects of sustainability (geospatial information, the legal, policies and institutional components). As part of the approach, in the first phase of the study, qualitative data were collected through a review of published information on the SDGs and the content of the subjects available in the subject handbook. Subject codes were assigned to the keywords and key phrases extracted from the SDGs and the subject content, and then compared and matched to establish the link between the subjects and the SDGs. Six university schools offering over 2,157 subjects were investigated. In the second phase, a survey was conducted involving subject coordinators with the purpose of validating the findings of the first phase and determining the strength of the linkages between the subjects and the SDGs. In the third phase, a plugin was designed to be used in the digital twin platform developed in the UoM, allowing visualisation of the research outcomes. Findings: Based on the interim findings, it was found that some subjects within the schools are linked to more than one SDG. However, not all of the subjects within the schools can be linked to the SDGs. There is a scope of improvement for embedding sustainability in more subjects within the schools. Some of the schools were also found to have weak linkages with sustainability, which demonstrate the challenge in technical subjects in linking their subject contents with sustainability. Originality/value: This study provides a methodology which enables the integration of sustainability into current state of the curricula at the university to be established. Further, with the advancement of geospatial technology and new visualisation opportunities through the use of the digital twin platform provides capabilities to communicate the outcomes of sustainability and involvement of each faculties and departments more effectively to the university community and wider stakeholders. [ABSTRACT FROM AUTHOR]

There are different processes in 3D urban land administration in which spatial analysis plays an underpinning role. Among 3D data models, Industry Foundation Class (IFC) provides the potential capabilities for modelling legal and physical dimensions of urban properties. However, performing spatial analysis using IFC files cannot address the on-demand spatial analysis requirements of 3D urban land administration. In response to this limitation, 3D data needs to be stored in a spatial database to enable spatial analyses required by different stakeholders. Therefore, in this paper, by considering spatial analysis requirements in 3D-enabled urban land administration, an IFC-based database schema is designed. Moreover, a methodology for transforming Building Information Modelling (BIM) data into the proposed schema is provided. This methodology includes seven steps: designing the architectural model and adding legal data, georeferencing, IFC data validation and cleaning, mapping process, database data validation and cleaning, spatial analysis, and visualisation. To demonstrate the feasibility of the proposed database, three datasets are implemented in the database. Moreover, a new method for modelling legal spaces with oblique structures and two applications of spatial analysis in 3D urban land administration are provided. [ABSTRACT FROM AUTHOR]

The utilization of urban underground space (UUS) offers an effective solution to urban problems but may also negatively affect urban development. Therefore, UUS development needs better concerted guidelines to coordinate various urban systems and the multiple components of the underground world. Sustainable Development Goals (SDGs), which should be viewed as important yardsticks for UUS development, do not explicitly mention urban underground space, although many of them are affected by both the positive and negative consequences of its development. To fill this gap, this review lays the foundations of relevant UUS concepts and uses exemplary cases to reveal that 11 out of 17 SDGs can be linked with UUS uses. These linkages also manifest that land administration, integrated planning, architectural design, and construction technology are critical dimensions for increasing the contributions of UUS to the realization of SDGs. To achieve multi-disciplinary synergies among these four critical dimensions, a collaborative approach framework based on spatial data infrastructure is required. Thus, this work provides academics and practitioners with a holistic view of sustainable UUS development. [ABSTRACT FROM AUTHOR]

The world's rising urban density expansion has resulted in a proliferation of attempts to efficiently use space and a higher level of spatial complexity in metropolitan areas. 3D geospatial data models are increasingly being embraced to facilitate communicating the spatial dimensions of complex built environments in different applications. For example, the use of 3D models in land administration systems has been recognized as a good approach for communicating the spatial complexity of legal spaces within multi‐storey buildings. The spatial extent of legal space—to which rights, restrictions and responsibilities relate in a 3D digital cadastre—needs to be accurately defined and geometrically closed; watertight. Therefore, this study aims to address the challenges regarding checking the closure of diverse 3D legal spaces and engage several techniques to formulate the watertight concept for cadastre. The research's methodology is built on a 3D polyhedral surface using a half‐edge data structure. A primitive check is employed to assess the spatial consistency of lower‐dimensional primitives of 3D objects. Subsequently, advanced closure checks ensure the closure of volumetric legal spaces represented by 2‐manifold and non‐2‐manifold data models. The article concludes that, by adopting the proposed approaches, the internal spatial consistency of legal spaces in urban land administration will be certified. [ABSTRACT FROM AUTHOR]

With the growth of high-density living, disputes experienced by residents in multi-owned buildings (MOBs) have become an ongoing challenge in urban areas. A significant number of the disputations have found their root cause in the issues concerning improper use and management of MOBs by residents. It stems from their inaccurate understanding of ownership rights that are inherently 3D but using 2D cadastral survey plans, authoritative documents of ownership. This research explores the ability of building information modeling (BIM) to address required information for improving the perception of ownership rights that affect resident behaviors in managing MOBs. An open data model of BIM is extended to accommodate the necessary information for preventing resident misbehaviors that led to dispute cases in Victoria state, Australia. In this study, we implement BIM data of an MOB where a real dispute happened to demonstrate the validity of the enriched data model on the information delivery and an enhanced understanding of ownership rights. It is confirmed that the use of BIM facilitates the alleviation of the misbehaviors by informing residents with accurate communication of ownership rights and could support the avoidance of disputes in MOBs. [ABSTRACT FROM AUTHOR]

The detection of spatial clusters and outliers is critical to a number of spatial data analysis techniques. Many techniques embed spatial clustering components with the aim of exploring spatial variability and patterns in a data set, caused by the spatial association that generally affects most spatial data. A frontier challenge in spatial data analysis is to extend techniques—originally designed for univariate analysis—to a multivariate context, in order to be able to cope with the increasing complexity and variety of modern spatial data. This article proposes an exploratory procedure to detect and classify clusters and outliers in a multivariate spatial data set. Cluster and outlier detection relies on recently introduced multivariate extensions of the well‐established local indicators of spatial association statistics. Two new indicators are proposed enabling the classification of multivariate clusters and outliers, not directly achievable with any already established technique. The procedure is fully implemented using free and open source geospatial software and libraries. The raw source code is made available for future reviews and replications. Empirical results from early applications on both synthetic and real spatial data are discussed. Advantages and limitations of the introduced procedure are outlined according to the empirical results. [ABSTRACT FROM AUTHOR]

The operational data of advanced process systems have met with explosive growth, but its fluctuations are so slight that the number of the extracted representative samples is quite limited, making it difficult to reflect the nature of the process and to establish prediction models. In this study, inspired by the process of fisherman repairing nets, a Kriging-based virtual sample generation (VSG) named Kriging-VSG is proposed to generate feasible virtual samples in data sparse regions. Then, the accuracy of prediction models is further enhanced by applying the generated virtual samples. In order to reasonably find data sparse regions, a distance-based criterion is imposed on each dimension to identify important samples with large information gaps. Similar to the process of fisherman repairing nets, a certain dimension is initially fixed at different quantiles. A dimension-wise interpolation process using Kriging is then performed on the center between important samples with large information gaps. To validate the performance of the proposed Kriging-VSG, two numerical simulations and a real-world application from a cascade reaction process for high-density polyethylene are carried out. The results indicate that the proposed Kriging-VSG outperforms other methods. [ABSTRACT FROM AUTHOR]

In this paper, an optimisation model for recovery planning of road networks is presented in which both social and economic resilience is aimed to be achieved. The model is formulated as a bi-level multi-objective discrete network design problem which forms a non-convex mixed integer non-linear problem. Solved by a Branch and Bound method, the solution algorithm employs an outer approximation method to estimate the lower bound of each node in the Branch and Bound search tree. The solution algorithm exploits a unique approach for lower-bound computation dealing with a disrupted multi-class network that may not be able to satisfy the demand between all OD pairs due to damaged links. The model is assessed by applying it on the Sioux Falls network. It is also illustrated how the Pareto-optimal solutions achieved by the multi-objective optimisation can vary depending on the emphasis placed on different classes of vehicles. [ABSTRACT FROM AUTHOR]

In the cadastral system of Victoria, the legal extent of properties is defined by a wide range of property boundaries referencing physical objects. These boundary types are typically represented in 2D plans. However, to address the challenges of 2D plans in complex buildings, there has been a growing trend towards the adoption of 3D models. These 3D models have been mainly used for visualization purposes, with no spatial query developed for identifying property boundaries in 3D models. Among 3D models, the Building Information Modelling (BIM) environment provides the potential capabilities for modelling property boundaries. In this paper, a spatial query approach predicated on topological relationships between a legal space and physical objects is developed to identify four types of property boundaries. A BIM model based on a case study in Victoria is used for implementation of the developed approach and the retrieved boundaries related to several properties with different levels of structural complexities are represented. In addition, various challenges during this process such as the impact of different design methods, and issues related to balconies and doors are discussed. The importance of this study is highlighted by a common scenario related to querying property boundaries. [ABSTRACT FROM AUTHOR]

This paper presents interim findings of a research project that was aimed to develop reliable methods to assess the condition of bridges and port-structures. The use of different types of non-destructive testing (NDT) equipment in assessing in situ concrete such as that for concrete cover measurement and locating the arrangement of the reinforcements, air permeability, electrical resistivity and the half-cell potential is reported in this paper. Six electrically inter-connected reinforced concrete (RC) specimens were made under laboratory conditions to validate NDT equipment output and to correlate them. Field testing was then conducted on the exterior of an RC wall of Doug McDonell Building at the University of Melbourne, Parkville campus. The results show that the use of multiple NDT equipment can enhance the understanding of the in situ condition by minimising the limitation inherent in each of the equipment. Combination of output from different testing methods allows a more precise condition assessment of the RC structure. In addition, measurements can be used to estimate the service life of the RC element. [ABSTRACT FROM AUTHOR]

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects. [ABSTRACT FROM AUTHOR]

Using state‐level annual variation in natural disasters and economic output in Australia, we estimate the direct effects of floods and bushfires on sectoral gross value added during the period 1978–2014. We find that floods exert an adverse and persistent effect on the outputs of agriculture, mining, construction and financial services sectors. For example, our estimates indicate that a state that experienced a flood in a given year encountered, on average, 5–6 per cent lower agricultural output in both that year and the following year, compared to another state with no such flood experience. Sectoral responses to bushfires are more nuanced. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) spatial information models are increasingly being adopted to help communicate the spatial dimensions of complex built environments. Land administration practices in multi-owned developments include the subdivision, registration and management of legal interests associated with private, communal and public properties, which are often located along the vertical dimension. The spatial structure of each legal interest is normally composed of invisible spaces, defined as the inside and outside of multi-owned developments, as well as physically built structures. Additionally, a wide variety of legal boundary types mark out the spatial limits of the individual parts of each legal interest. These legal boundaries are typically delineated by either relying on fixed surveying measurements or referencing physically existent objects. This article provides a critical assessment of 3D spatial information models in terms of their capabilities for modelling legal interests and legal boundaries defined in the Victorian jurisdiction. We classify these models into three categories: purely legal, purely physical and integrated. This assessment provides the basis for developing a new 3D spatial information model, which would subsequently support a pathway towards realising the Victorian land administration system in a 3D digital environment. [ABSTRACT FROM AUTHOR]

Modelling people behaviour during emergencies has become an essential issue in attempting to increase safety aspects in buildings. This paper evaluates people’s choice behaviour for evacuation of tall buildings. A Stated Preference (SP) questionnaire was designed to understand underlying factors behind people behaviour and predict the likelihood of selecting evacuation lifts as opposed to stairs. Various scenarios including six different navigational cases, three levels for the density of people on stairs, three different number of people in the lift lobby and three vertical positions for refuge floors were administrated to 566 participants. A mixed logit model approach was then used to investigate how those factors influence the occupant’s decision-making as well as to capture the heterogeneity of different preferences among people. Traditionally, lifts were not allowed to be used in case of emergency, but the results indicate that people would tend to choose evacuation lifts in situations when they are suggested as the main exit option, and situations when stairs are overcrowded. Thus, if people are navigated by dynamic signs to use evacuation lifts, the percentage of lift users could go approximately from 70% to 80% for refuge floors between 15 and 55, respectively. In contrast, in situations when people have to make a decision between using lifts or stairs to evacuate, stairwells with fewer people as well as overcrowded refuge floors could lead to a decision in favour of stairs. This study represents the first SP experiment combining people decisions, pre-event opinions and beliefs related to evacuation lifts and stairs to understand their route choices for evacuation from tall buildings. The findings of this study can be used in the development of behavioural models for evacuation simulations of tall buildings. [ABSTRACT FROM AUTHOR]

ePlan, as a digital cadastral data initiative, is a collaborative program between the land authorities and the surveying industry which aims to replace paper and PDF cadastral plans and surveys with digital data. ePlan is currently operational in Australia, New Zealand and Singapore. ePlan was introduced in the State of Victoria in 2011 and has been operational in this jurisdiction for 2D plans since 2013. On average, one ePlan application is currently submitted to a digital plan lodgment portal every two weeks. The low uptake of ePlan is caused by several technical and non-technical challenges. This paper provides an overview of cadastral information transitioning from paper to digital in Victoria. The research methodology to identify the challenges in Victoria for the adoption of ePlan is then described. This is followed by a discussion on the identified challenges. The paper then proposes a generic framework of strategic actions to increase the uptake of digital cadastral data based on the lessons learned from Victoria. The initiatives suggested by this framework to address the ePlan challenges in Victoria and increase its uptake are also introduced. The paper concludes with a direction for future research. [ABSTRACT FROM AUTHOR]

Objectives: Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle-pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle-pedestrian crashes to identify the hotspots and hot times.Methods: Data for all vehicle-pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle-pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and genders.Results: Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars.Conclusions: This research reveals that temporal and spatial distributions of vehicle-pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle-pedestrian crashes. [ABSTRACT FROM AUTHOR]

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods. [ABSTRACT FROM AUTHOR]

The increasing amount of spatial data demands effective and efficient methods for managing them in spatial data infrastructures (SDIs). Metadata standards have been developed in response to the needs of data custodians who manage these ever-growing resources and in recognition of the end users’ need for easy access and discovery of spatial data. Data custodians prepare metadata records and allow SDIs to harvest them. The harvesting can be undertaken through metadata standards or data service standards. The metadata updating processes are not efficient and, therefore, create a disjoint between the data and its metadata. As a result, end users cannot realize the full potential of metadata records. In addressing this issue, the paper presents an automatic spatial metadata system with a suite of functions including metadata monitoring, enrichment, and maintenance to enable the data custodians to manage the integrity of their metadata records in SDIs. These functions have been demonstrated and reviewed through a case study in the Australian Urban Infrastructure Network (AURIN). Using these functions, AURIN’s data custodians have been able to offer over 1200 coherent and consistent spatial metadata records to end users. [ABSTRACT FROM PUBLISHER]

Connected vehicles (CVs), be they autonomous vehicles or a fleet of cargo carriers or Uber, are a matter of when they become a reality and not if. It is not unreasonable to think that CV technology may have a far-reaching impact, even to the genesis of a completely new traffic pattern. To this end, the literature has yet to address the routing behavior of the CVs, namely traffic assignment problem (TAP) (perhaps it is assumed, they ought to follow the traditional shortest possible paths, known as user equilibrium [UE]). It is possible that real-time data could be derived from the vehicles' communications that in turn could be used to achieve a better traffic circulation. In this article, we propose a mathematical formulation to ensure the CVs are seeking the system optimal (SO) principles, while the remainder continue to pursue the old-fashioned UE pattern. The model is formulated as a nonlinear complementarity problem (NCP). This article contributes to the literature in three distinct ways: (i) mathematical formulation for the CVs' routing, stated as a mixed UE-SO traffic pattern, is proposed; (ii) a variety of realistic features are explicitly considered in the solution to the TAP including road capacity, elastic demand, multiclass and asymmetric travel time; and (iii) formal proof of the existence and uniqueness of the solutions are also presented. The proposed methodology is applied to the networks of Sioux-Falls and Melbourne. [ABSTRACT FROM AUTHOR]

Multistorey buildings typically include stratified legal interests which provide entitlements to a community of owners to lawfully possess private properties and use communal and public properties. The spatial arrangements of these legal interests are often defined by multiplexing cognitively outlined spaces and physical elements of a building. In order to support 3D digital management and communication of legal arrangements of properties, a number of spatial data models have been recently developed in Geographic Information Systems (GIS) and Building Information Modelling (BIM) domains. While some data models, such as CityGML, IndoorGML or IFC, provide a merely physical representation of the built environment, others, e.g., LADM, mainly rely on legal data elements to support a purely legal view of multistorey buildings. More recently, spatial data models integrating legal and physical notions of multistorey buildings have been proposed to overcome issues associated with purely legal models and purely physical ones. In previous investigations, it has been found that the 3D digital data environment of BIM has the flexibility to utilize either only physical elements or only legal spaces, or an integrated view of both legal spaces and physical elements to represent spatial arrangements of stratified legal interests. In this article, the performance of these three distinct BIM-based representations of legal interests defined inside multistorey buildings is assessed in the context of the Victorian jurisdiction of Australia. The assessment metrics are a number of objects and geometry batches, visualization speed in terms of frame rate, query time, modelling legal boundaries, and visual communication of legal boundaries. [ABSTRACT FROM AUTHOR]