Search results

This paper aims to analyze the current state of technological advancements research in addressing the diverse risk factors involved in earthmoving equipment operations through Rasmussen's (1997) risk management framework. It examines how existing technologies research capture, manage and disseminate risk information across various levels of safety management by defining their core functionalities. The research highlights gaps in current technological solutions research regarding the flow of information in the risk management framework. It emphasizes the need for an integrated approach in technological advancements to enhance the holistic safety management approach capable of capturing various risks across different levels of risk management.

This research employs a multistep approach. Initially, earthmoving equipment risk factors and functionalities of technological solutions were identified through a systematic review of current scholarly works. Subsequently, social network analysis (SNA) and Pareto analysis were applied to evaluate and determine the importance of risk factors and functionalities of technologies for improving them.

The findings highlight the importance of multilevel approaches that expand technological functionalities to address risk factors across all levels of Rasmussen's (1997) risk management framework. The current combination of technological advancements focuses primarily on on-site monitoring, congested work sites, site layout/path planning, utility problems, safety training, and blind spot and visibility. Site monitoring and warning systems, supported by sensors and computer vision (CV), are pivotal for identifying risks and enabling data-driven safety management. However, workforce-level cognitive factors (W1-W6), which influence safety behavior, remain underexplored for enhancing their functionality to anticipation and response during the operation. Prevention is the core function of current technological solutions, emphasizing the need to address human and equipment risk factors such as sources of hazards in earthmoving operations. Learning: AI as a data-driven approach and IoT systems are key for future development, and when grounded in ontology-based knowledge of earthwork, they gain a structured vision of earthmoving equipment types, their interactions and the earthwork activities. It enhances the capabilities of these technologies to capture and manage complex interactions between hazard sources (human and equipment), supporting comprehensive risk factors across all levels of the risk management framework.

This paper elucidates that technological solutions for safety management in earthmoving equipment operations require a more holistic approach—grounded in an understanding of functionalities of technologies—to effectively capture risks across various levels of Rasmussen (1997) risk management. It emphasizes that technological solutions should not only address isolated hazards but also ensure the continuous flow of information on multiple risk factors across the risk management framework.

The integration and automation of the whole design and implementation process have become a pivotal factor in construction projects. Problems of process integration, particularly at the conceptual design stage, often manifest through a number of significant areas, from design representation, cognition and translation to process fragmentation and loss of design integrity. Whilst building information modelling (BIM) applications can be used to support design automation, particularly through the modelling, amendment and management stages, they do not explicitly provide whole design integration. This is a significant challenge. However, advances in generative design now offer significant potential for enhancing the design experience to mitigate this challenge.

The approach outlined in this paper specifically addresses BIM deficiencies at the conceptual design stage, where the core drivers and indicators of BIM and generative design are identified and mapped into a generative BIM (G-BIM) framework and subsequently embedded into a G-BIM prototype. This actively engages generative design methods into a single dynamic BIM environment to support the early conceptual design process. The developed prototype followed the CIFE “horseshoe” methodology of aligning theoretical research with scientific methods to procure architecture, construction and engineering (AEC)-based solutions. This G-BIM prototype was also tested and validated through a focus group workshop engaging five AEC domain experts.

The G-BIM prototype presents a valuable set of rubrics to support the conceptual design stage using generative design. It benefits from the advanced features of BIM tools in relation to illustration and collaboration (coupled with BIM's parametric change management features).

This prototype has been evaluated through multiple projects and scenarios. However, additional test data is needed to further improve system veracity using conventional and non-standard real-life design settings (and contexts). This will be reported in later works.

Originality and value rest with addressing the shortcomings of previous research on automation during the design process. It also addresses novel computational issues relating to the implementation of generative design systems, where, for example, instead of engaging static and formal description of the domain concepts, G-BIM actively enhances the applicability of BIM during the early design stages to generate optimised (and more purposeful) design solutions.

This study aims to support the global initiatives that advocate for ensuring healthy lives and promoting well-being for everyone, regardless of age, while allowing people to stay at their homes as long as they desire. The built environment (BE) plays a crucial role in achieving this, but in some countries, such as the UK, the housing stock has been found to require extensive adaptations to support resident’s health and well-being. While much research has been done on care provisions and later living housing, these solutions are unsuitable for low-population density areas (LPDAs).

The study is encompassed by investigations around a systematic product development guided by the Double-Diamond Design Framework. This research focused on the “Discovery” phase, which involved online in-depth interviews, incorporating elements from the Human-Activity-Space-Technology Model, supplemented by an interactive board to discover key activities, elements and actors involved in supporting strategies for ageing in place.

This paper presents strategies to help people age in place, focusing on LPDAs. The interventions identified in this paper encompass fundamental elements such as layout design and smart home technologies.

The results provide contextualised BE interventions applicable to creating age-friendly communities, focusing on house design and service delivery from a product design approach.