Vicente A. González

Ricardo Antunes, Vicente González, Kenneth Walsh

Based on the knowledge of dynamic systems, the shorter the transient response, or the faster a system reaches the steady-state after the introduction of the change, the smaller will be the output variability. In lean manufacturing, the principle of reducing set-up times has the same purpose: reduce the transient time and improve production flow. Analogously, the analysis of the transient response of project-driven systems may provide crucial information about how fast these systems react to a change and how that change affects their production output. Although some studies have investigated flow variability in projects, few have looked at variability from the perspective that the transient state represents the changeovers on project-driven production systems and how the transient state affects the process' flow variability. The purpose of this study is to investigate the effect of changes in project-driven production systems from a conceptual point of view, furthermore, measuring and correlating the transient response of five cases to their flow variability. Results showed a proportional relationship between the percentile transient time and flow variability of a process. That means that the quicker the production system reacts to change; the less the distress in the production output, consequently, lower levels of flow variability. As practical implications, lean practices focusing on reducing set-up times (transient time) can have their effects measured on project-driven production flow.

Ricardo Antunes, Vicente A. González, Kenneth Walsh

Projects are finite terminating endeavors with distinctive outcomes, usually, occurring under transient conditions. Nevertheless, most estimation, planning, and scheduling approaches overlook the dynamics of project-based systems in construction. These approaches underestimate the influence of process repetitiveness, the variation of learning curves and the conservation of processes' properties. So far, estimation and modeling approaches have enabled a comprehensive understanding of repetitive processes in projects at steady-state. However, there has been little research to understand and develop an integrated and explicit representation of the dynamics of these processes in either transient, steady or unsteady conditions. This study evaluates the transfer function in its capability of simultaneously identifying and representing the production behavior of repetitive processes in different state conditions. The sample data for this research comes from the construction of an offshore oil well and describes the performance of a particular process by considering the inputs necessary to produce the outputs. The result is a concise mathematical model that satisfactorily reproduces the process' behavior. Identifying suitable modeling methods, which accurately represent the dynamic conditions of production in repetitive processes, may provide more robust means to plan and control construction projects based on a mathematically driven production theory.

Muhammad Adil, Gaang Lee, Vicente A. Gonzalez et al.

Safety hazard identification and prevention are the key elements of proactive safety management. Previous research has extensively explored the applications of computer vision to automatically identify hazards from image clips collected from construction sites. However, these methods struggle to identify context-specific hazards, as they focus on detecting predefined individual entities without understanding their spatial relationships and interactions. Furthermore, their limited adaptability to varying construction site guidelines and conditions hinders their generalization across different projects. These limitations reduce their ability to assess hazards in complex construction environments and adaptability to unseen risks, leading to potential safety gaps. To address these challenges, we proposed and experimentally validated a Vision Language Model (VLM)-based framework for the identification of construction hazards. The framework incorporates a prompt engineering module that structures safety guidelines into contextual queries, allowing VLM to process visual information and generate hazard assessments aligned with the regulation guide. Within this framework, we evaluated state-of-the-art VLMs, including GPT-4o, Gemini, Llama 3.2, and InternVL2, using a custom dataset of 1100 construction site images. Experimental results show that GPT-4o and Gemini 1.5 Pro outperformed alternatives and displayed promising BERTScore of 0.906 and 0.888 respectively, highlighting their ability to identify both general and context-specific hazards. However, processing times remain a significant challenge, impacting real-time feasibility. These findings offer insights into the practical deployment of VLMs for construction site hazard detection, thereby contributing to the enhancement of proactive safety management.

Yifan Gao, Vicente A. Gonzalez, Tak Wing Yiu et al.

The prediction of workers' safety behaviour can help identify vulnerable workers who intend to undertake unsafe behaviours and be useful in the design of management practices to minimise the occurrence of accidents. The latest literature has evidenced that there is within-population diversity that leads people's intended safety behaviours in the workplace, which are found to vary among individuals as a function of their personality traits. In this study, an innovative forecasting model, which employs neural network algorithms, is developed to numerically simulate the predictive relationship between construction workers' personality traits and their intended safety behaviour. The data-driven nature of neural network enabled a reliable estimate of the relationship, which allowed this research to find that a nonlinear effect exists in the relationship. This research has practical implications. The neural network developed is shown to have highly satisfactory prediction accuracy and is thereby potentially useful for assisting project decision-makers to assess how prone workers are to carry out unsafe behaviours in the workplace.

Zhenan Feng, Vicente A. González, Ling Ma et al.

Virtual Reality (VR) for Serious Games (SGs) is attracting increasing attention for training applications due to its potential to provide significantly enhanced learning to users. Some examples of the application of VR for SGs are complex training evacuation problems such as indoor earthquake evacuation or fire evacuation. The indoor 3D geometry of existing buildings can largely influence evacuees' behaviour, being instrumental in the design of VR SGs storylines and simulation scenarios. The VR scenarios of existing buildings can be generated from drawings and models. However, these data may not reflect the 'as-is' state of the indoor environment and may not be suitable to reflect dynamic changes of the system (e.g. Earthquakes), resulting in excessive development efforts to design credible and meaningful user experience. This paper explores several workflows for the rapid and effective reconstruction of 3D indoor environments of existing buildings that are suitable for earthquake simulations. These workflows start from Building Information Modelling (BIM), laser scanning and 360-degree panoramas. We evaluated the feasibility and efficiency of different approaches by using an earthquake-based case study developed for VR SGs.

Ruggiero Lovreglio, Vicente Gonzalez, Zhenan Feng et al.

Enhancing evacuee safety is a key factor in reducing the number of injuries and deaths that result from earthquakes. One way this can be achieved is by training occupants. Virtual Reality (VR) and Serious Games (SGs), represent novel techniques that may overcome the limitations of traditional training approaches. VR and SGs have been examined in the fire emergency context, however, their application to earthquake preparedness has not yet been extensively examined. We provide a theoretical discussion of the advantages and limitations of using VR SGs to investigate how building occupants behave during earthquake evacuations and to train building occupants to cope with such emergencies. We explore key design components for developing a VR SG framework: (a) what features constitute an earthquake event, (b) which building types can be selected and represented within the VR environment, (c) how damage to the building can be determined and represented, (d) how non-player characters (NPC) can be designed, and (e) what level of interaction there can be between NPC and the human participants. We illustrate the above by presenting the Auckland City Hospital, New Zealand as a case study, and propose a possible VR SG training tool to enhance earthquake preparedness in public buildings.