Construction site safety control with medium-accuracy location data (original) (raw)
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Existing deterministic approaches to real-time tracking of construction workers for safety purposes provide alerts for specific incidents that are isolated in space and time, once they have occurred. In contrast, this paper presents a statistical model that can support a more dynamic form of safety control, by utilizing real-time tracking data to control the exposure of construction workers to safety risks that accumulate and change over time. The model addresses risks that are the result of concurrent activities on the construction site, and provides proactive alerts in case of an increasing risk exposure for a worker or crew. Statistical zones that are related to medium risk areas on the site are defined in the model. A number of statistical rules are then used to identify deviations from a predefined maximum allowable risk exposure for workers located in the statistical zones. The model can thus prevent potential accidents from occurring, without unnecessarily affecting the efficiency of the activities carried out on site. Laboratory tests of the model were carried out, using a Wi-Fi-based RTLS. The results of the tests demonstrate that the model can identify an excessive exposure to risk for workers, support an initial analysis of the causes for the excessive risk exposure, and compensate for errors in the RTLS measurements.
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Full paper: A Bayesian model for real-time safety management in construction sites
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Purpose This article reports on an ongoing research project, which is aimed at implementing advanced probabilistic models for real-time identification of hazardous events at construction sites. The model has intelligent capabilities for near real-time automated recognition of hazardous events during the execution phase. To achieve this, features of Bayesian Networks have been exploited. In addition, inputs to the model are assumed to be provided by a pervasive monitoring system deployed on the site. The need for this kind of intelligent tool is determined by the complexity inherent in construction sites, due to a variety of reasons, such as heterogeneity of the actors, the simultaneous nature of operations, harsh contextual conditions, and the only partially efficient current approach based on health and safety plans. Hence, this model is proposed as a support tool for health and safety coordinators for supervision of sites as they cannot guarantee a continuous physical presence. Method Given that there are no long-time series on past occurrences of hazardous events in all the potential contextual combinations presently available, the probabilistic models cannot be learned just through datasets. For that reason, the available data have been integrated with expert opinions. In particular, the conditional probabilities of the Bayesian networks are estimated by an elicitation process of subjective knowledge from the opinions of experts. The complexity of the phenomena under analysis are modelled as a tree structure with several levels (corresponding to the work-breakdown structure hierarchy), which itself is based on the top-down technique; it provides therefore a clear view of the global picture. The built-hierarchical tree allows the expert to weigh more easily causal relationships involved and also to define the qualitative structure of the net. Furthermore, the article describes and tests how conditional probabilities of the variables in the networks can be estimated, through gathering and interviewing groups of stakeholders and experts. Results & Discussion Our research has led to the definition of a probabilistic model using elicitation techniques for subjective knowledge. Furthermore, the development of such a model is part of a wider system relying on the implementation of a real-time monitoring network.
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Automation in Construction, 2011
Construction sites still present high accident rates. The advent of advanced sensing technologies has motivated the development of unprecedented automated systems, capable of supporting health and safety control. Their application is particularly critical in those cases where regular safety inspections may prove to be inadequate in terms of preventing risks which, because of their specific nature, are unpredictable. This paper reports the development of a first prototype for the proactive safety management and real-time signaling of potential overhead hazards. It is expected to enhance standard safety policies and assist inspectors and coordinators in executing their tasks. The system performs real-time tracking using ultra wide band technology and implements proactive virtual fencing logics. The development of the system, its laboratory test results, the algorithm optimization, and the final field test results are reported herein. The results achieved demonstrate the capability of proactive logics to reinforce safety management policies and assist personnel in coping with unpredictable hazardous events.
SAFETY ANALYSIS OF WORK SPACES BY LOCATING MATERIALS IN BUILDING CONSTRUCTION SITES
A building construction site has many unsafe and dangerous factors such as an elevator pit, a slab opening for lifting forms and materials. Accidents happen unpredictably though warning and caution signs for the dangerous factors provide safe working environment to workers. The warning signs are likely to be hidden by stacked materials or temporary facilities on site. The workers cannot notice the signs when moving or working. This paper suggests a method to identify a problem that the workers can’t see the unsafe factors, and to manage the problem in construction planning phases with a worker’s view using a CAD software.