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The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

Roofing is one of the most dangerous jobs in the construction industry. Due to factors such as lack of planning, training and use of precaution, roofing contractors and workers continuously violate the fall protection standards enforced by the US Occupational Safety and Health Administration. A preferable way to alleviate this situation is automating the process of non-compliance checking of safety standards through measurements conducted in site daily accumulated videos and photos. As a key component, the purpose of this paper is to devise a method to detect roofs in site images that is indispensable for such automation process.

This method represents roof objects through image segmentation and visual feature extraction. The visual features include colour, texture, compactness, contrast and the presence of roof corner. A classification algorithm is selected to use the derived representation for statistical learning and detection.

The experiments led to detection accuracy of 97.50 per cent, with over 15 per cent improvement in comparison to conventional classifiers, signifying the effectiveness of the proposed method.

This study did not test on images of roofs in the following conditions: roofs initially built without apparent appearance (e.g. structural roof framing completed and undergoing the sheathing process) and flat, barrel and dome roofs. From a standpoint of construction safety, while the present work is vital, coupling with semantic representation and analysis is still needed to allow for risk analysis of fall violations on roof sites.

This study is the first to address roof detection in site images. Its findings provide a basis to enable semantic representation of roof site objects of interests (e.g. co-existence and correlation among roof site, roofer, guardrail and personal fall arrest system) that is needed to automate the non-compliance checking of safety standards on roof sites.

The materials included in this column were selected on the basis of an item by item review of all government publications received at a major depository library. Emphasis is placed on tools of a reference format, although much else that the Federal government publishes is otherwise of high referral value. Publications not in a reference format, in the strictest sense, are included when their potential value dictates; brief entries for “how to” items and informative pamphlets are also given if deemed potentially useful, particularly for school and public libraries and wherever the identification of vertical file materials is the responsibility of the public service librarian.

Attempts to develop a model of maintenance decision making using the analytic hierarchy (AHP). Describes problems in maintenance arising from not having clear criteria and not having robust decisions with which to maintain failing equipment. The objective being to develop a dynamic and adaptable maintenance system that utilises existing data and supports decisions accordingly. Proposes a three‐stage system that can handle multiple criteria decision analysis, conflicting objectives, and subjective judgements. Moreover, the methodology facilitates and supports a group decision‐making process. This systematic, and adaptable, approach will determine what specific actions to perform given current working conditions. The first stage involves identifying the criteria upon which engineering personnel wish to formulate a maintenance decision, or action. The second stage is to prioritise the different criteria by implementing a multiple‐criteria evaluation method. Finally, based on different criteria, machines are ranked according to criticality. This is followed by an analysis of failures in a graphical and a hierarchical format.

Rob Knowles has been appointed Technical Service Specialist by Metech‐Ronal to support the company's rapidly expanding Thick Film Materials business throughout Europe.

This study aims to examine on-site particle concentration levels due to emissions from a wide spectrum of additive manufacturing techniques, including polymer-based material extrusion, metal and polymer-based powder bed fusion, directed energy deposition and ink-based material jetting.

Particle concentrations in the operating environments of users were measured using a combination of particle sizers including the TSI 3910 Nano SMPS (10–420 nm) and the TSI 3330 optical particle sizer (0.3–10 µm). Also, fumes from a MEX printer during printing were directly captured using laser imaging method.

The number and mass concentration of submicron particles emitted from a desktop open-type MEX printer for acrylonitrile-butadiene-styrene and polyvinyl alcohol approached and significantly exceeded the nanoparticle reference limits, respectively. Through laser imaging, fumes were observed to originate from the printer nozzle and from newly deposited layers of the desktop MEX printer. On the other hand, caution should be taken in the pre-processing of metal and polymer powder. Specifically, one to ten micrometers of particles were observed during the sieving, loading and cleaning of powder, with transient mass concentrations ranging between 150 and 9,000 µg/m³ that significantly exceeded the threshold level suggested for indoor air quality.

Preliminary investigation into possible exposures to particle emissions from different 3D printing processes was done, which is useful for the sustainable development of the 3D printing industry. In addition, automatic processes that enable “closed powder cycle” or “powder free handling” should be adopted to prevent users from unnecessary particle exposure.

One of the common law duties owed by the employer is his duty to take reasonable care for the safety of his employee. This common law duty is an implied term in the contract of employment and is therefore contractual in nature. Because of the difficulties which may arise in bringing an action in contract for breach of the employer's duty of care, the employee who has sustained injuries during the course of his employment (although he may sue either in contract of tort will normally bring a tort action.

Knight's Industrial Law Reports goes into a new style and format as Managerial Law This issue of KILR is restyled Managerial Law and it now appears on a continuous updating basis rather than as a monthly routine affair.