Robin Drogemuller

Efficient data management is critical for operational and regulatory success in the utility sector. However, persistent challenges from fragmented, inconsistent, and poorly governed data continue to hinder progress. Despite technological advancements, the sector lacks cohesive governance structures and effective metadata management practices to ensure data accuracy, traceability, and interoperability. This study explores semantic web technologies and graph databases to mitigate data fragmentation and enable concise data representation in the maintenance phase. A novel data governance framework is developed, emphasizing metadata – particularly provenance aspects such as lineage and stewardship – to strengthen intra- and inter-organizational governance within the Australian utilities sector. The research integrates Design Science Research (DSR) and Grounded Theory Method (GTM). Problem identification involved literature review, stakeholder interviews, review of Before You Dig Australia (BYDA) maps, and application of the maturity model DAMA-DMBOK2 to assess the current state of the sector’s data management practices and for the development of a roadmap towards improvement. In solution design, the Utility Operations and Maintenance Ontology (UOMO) was extended to incorporate lineage tracking, standardized GIS-based data sharing, and accountability assignment. Two real-world case studies validated the framework’s effectiveness. This study makes four original contributions: operationalizing semantic web technologies within a sector-specific governance framework; introducing provenance-driven ontological approaches; demonstrating property graph data models for interconnected utility data; and integrating DSR with GTM to deliver practically grounded solutions. The research bridges conceptual models and implementation, offering Australian utilities a viable approach to meet operational and regulatory demands.

Accurate rooftop segmentation from aerial imagery is essential for large-scale urban analysis, including applications such as solar potential assessment and urban monitoring. However, it remains constrained by the high cost of dense annotation and the limited generalisation of supervised models across heterogeneous urban morphologies. This study investigates binary rooftop segmentation for fine-tuning large image-editing foundation models using parameter-efficient Low-Rank Adaptation (LoRA). Using parts of Brisbane metropolitan dataset (split 80/20 into 97 training and 24 testing tiles), three paradigms were evaluated under a unified protocol: zero-shot image-editing models (including Gemini 3 Pro), a segmentation-first baseline (Segment Anything Model 3, SAM3), and LoRA-adapted diffusion models (FLUX.1 Kontext, FLUX.2, and Qwen Image Edit 2509) fine-tuned each 250 steps up to 5000 steps. Evaluated under zero-shot conditions, the generative models demonstrated varying levels of boundary fidelity. The Gemini model achieved a strong zero-shot baseline with [IoU, Dice] scores of [85%, 91%], followed by the SAM3 baseline, which also achieved a stable [84%, 91%] but exhibited increased false negatives in visually complex scenes. The tested diffusion models (FLUX.1 Kontext, FLUX.2, and Qwen) showed more limited initial spatial overlap, scoring [45%, 55%], [67%, 78%], and [33%, 46%], respectively. Following LoRA adaptation, the FLUX and Qwen models showed substantial improvements, with their respective [IoU, Dice] metrics increasing to [89%, 94%], [82%, 90%], and [87%, 93%]. FLUX.1 Kontext achieved the strongest overall performance at step 4250, yielding a mean IoU of 89% (SD = 3.16%) and a pixel accuracy exceeding 96%. These results demonstrate that parameter-efficient fine-tuning, combined with rigorous evaluation under class-imbalanced conditions, can transform general-purpose generative models into competitive, scalable spatial analysis tools that match or exceed both dedicated segmentation baselines and strong zero-shot multimodal models.

Background: As the global demographic shifts towards an ageing population, the significance of the built environment in facilitating ageing in place gains prominence. This study addresses the critical question of how a built environment can support older adults’ independence and enhance their quality of life. Method: Utilising a systematic review of review papers, this research scrutinises existing literature to uncover the influence of urban planning, physical accessibility, and residential design on the older people’s physical and mental well-being. More specially, the study methodically examines existing review articles that span various aspects of the built environment related to ageing in place. Results: The analysis reveals that specific elements of the built environment, notably rational urban planning and accessible urban spaces, as well as adaptable housing designs, significantly impact ageing individuals’ health, social engagement, and overall satisfaction with life. It also identifies contradictions in the effects of environmental features on cognitive health and well-being, underscoring areas that warrant further exploration. Conclusions: The study concludes that thoughtful urban and housing designs are pivotal in creating age-friendly environments that support ageing in place. It emphasises the need for future research and policy development aimed at enhancing the living conditions of older adults within their communities.

Earthmoving operations in the construction process are complex environments that involve interactions between equipment, the workforce, and materials within an overarching construction plan. Over the past two decades, researchers in construction have focused on improving the safety of construction earthmoving equipment due to their omnipresence in the construction environment. Although previous studies have explored safety risks and the causes of accidents involving construction earthmoving equipment, their approaches were common and lacked a comprehensive perspective. Hence, this systematic literature review applies Rasmussen’s (1997) risk management framework using a systems thinking approach to identify and classify the risk factors influencing earthmoving equipment operation safety in construction sites. Utilizing a multistep methodology, this research first identifies 38 risk factors pertinent to earthmoving equipment operations and then classifies them based on systems thinking. Social network analysis (SNA) is employed to analyze the data. The results show that most research on earthmoving equipment safety focuses on monitoring construction sites, but very little on government and regulatory roles. When considering the interdependencies of risk factors, safety training is the most important factor, followed by the largely overlooked earthmoving machinery characteristics and manufacturer’s performance. The results of this review inform both the research community and industry practitioners regarding the less-understood aspects of earthmoving equipment operation safety and future research directions.

The benefits of adopting collaborative approaches in the construction industry, such as Building Information Modelling (BIM), Integrated Project Delivery (IPD), and Lean Construction (LC), in an integrated manner are widely acknowledged in academia and industry. Once organizations have embraced BIM, IPD, and LC integration (BIL), a measurement method for evaluating their progress and planning for continuous improvement is required. However, there is no widely accepted capability assessment model, such as Maturity Models (MMs), to effectively assess productivity improvements in organizations adopting all three approaches based on the interdependencies between them. The first step in the process of BIL MM development is identifying the critical attributes of BIM, IPD, and LC integration. This research investigates the interrelationships of BIM, IPD, and LC for use in the maturity models, determining how BIM and IPD can support the application of LC principles. The mixed methods approach was adopted, and a literature review and a two-round Delphi survey were conducted for the data collection. The findings revealed a number of complementarities between BIM, IPD, and LC. The panel of experts agreed upon the five major attributes and 24 sub-attributes of BIM, IPD, and LC, which formed the structure of the presented integrated BIL framework. The significance of this study is to provide the basis for organizations that intend to implement BIM, IPD, and LC in an integrated manner. Interrelating these attributes in a maturity framework also supports the planning and evaluation of organizations’ progress towards realistic goals for continuous improvement.

While the advantages of leveraging advanced technologies and Industry 4.0 for effective safety management have been extensively recognized, the journey towards a more mature integration of Industry 4.0 technologies into safety management practices often lacks a well-defined and systematic guidance map. This research is a step towards providing organizations with a structured approach to navigate and achieve successful safety management transformation, enabling them to fully harness the potential of industry 4.0 technologies in the workplace. Two rounds of systematic literature reviews (SLRs) are conducted to narrow down the number of articles based on the PRISMA method, which are then subjected to further content analysis. The study highlights the integration of Industry 4.0 technologies within the domains of People, Process, and Policy and their significance in advancing safety maturity. This research uncovered key themes, providing valuable insights that will shape the conceptual maturity model structure of safety management based on the innovative nature of Industry 4.0 to enhance their safety culture to align with. The results provide a fertile ground for a Smart Safety Maturity Model, to integrate technologies to elevate safety drivers in construction safety management