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Sensing- and warning-based technologies are widely used in the construction industry for occupational health and safety (OHS) monitoring and management. A comprehensive understanding of the different types and specific research topics related to the application of sensing- and warning-based technologies is essential to improve OHS in the construction industry. The purpose of this paper is to examine the current trends, different types and research topics related to the applications of sensing- and warning-based technology for improving OHS through the analysis of articles published between 1996 and 2017 (years inclusive).

A standardized three-step screening and data extraction method was used. A total of 87 articles met the inclusion criteria.

The annual publication trends and relative contributions of individual journals were discussed. Additionally, this review discusses the current trends of different types of sensing- and warning-based technology applications for improving OHS in the industry, six relevant research topics, four major research gaps and future research directions.

Overall, this review may serve as a spur for researchers and practitioners to extend sensing- and warning-based technology applications to improve OHS in the construction industry.

Smart-home technology (SHT) has been identified by the World Health Organization as a possible solution for assisting older people to maintain their independence and to live safely at home when performing the activities of daily living. This study aims to identify the factors, as well as their inter-relationships, influencing senior citizens to adopt elderly-friendly SHT that supports ageing-in-place in high-density Hong Kong living settings.

An interpretive structural modelling approach has been used to analyse the factors to develop a better understanding of the relationships between factors influencing SHT adoption, and “Matrice d’Impacts Croisés-Multiplication Appliquée à un Classement” analysis has been used to classify the analysed factors.

The results suggested that strong government support, efficient backup supporting service and the design of user interface devices have been found as the driving factors encouraging the adoption of SHT. Other factors, including the maintenance of devices, levels of usage and penetration of devices, individual needs and financial considerations, were considered as autonomous factors and are less important to the decision to adopt SHT.

This study provides useful information to policymakers and building designers on the human perspective of SHT adoption, such as the needs and requirements of older people to be considered in SHT technical design and appropriate technological solutions.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM).

To improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system.

The system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making.

This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in FM practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually and improves communication and collaboration between FM personnel for maintenance in the decision-making process.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.

This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.

Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.

The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

This study aims to understand the impacts of the Minor Works Control System (MWCS) on the performance of minor works contractors following its implementation in 2011, and specifically the initiatives adopted by minor works contractors in response to the new building control regime. Suggestions are made for the further improvement of the MWCS. Like many Western countries and Asian counterparts, Hong Kong has recently implemented a new building control system (the MWCS), which aims to restructure the building proposal approval process and shift the responsibility for building control from the public to the private sector. The effectiveness of the MWCS has been strongly questioned by the industry and the public.

A mixed method including a questionnaire survey (quantitative) and focus group discussions (qualitative) was adopted to provide an initial evaluation of the impact of the MWCS on practitioners and the industry.

The results suggest that implementation of the new control system has helped increase safety awareness and the technical capacity of minor works contractors. Despite these benefits, registered contractors are encountering challenges under the MWCS, such as manpower arrangement problems and higher business operating costs. Initiatives that include maintaining a sound financial background, an adequate in-house supervisory staff and a safe working environment are considered critical by practitioners to maintain their competitive edge under the new control regime.

This study is one of the first studies in Hong Kong to evaluate the impact of the new building control system. The feedback and suggestions provided by the practitioners and experts during the research provide valuable insights for the government on how to provide support to practitioners under the MWCS to achieve a better built environment in Hong Kong.

The construction industry has been criticized for cultural intolerance and its poor industrial image. The ethnically diverse construction workplace in Hong Kong (HK) is frequently noted as a place in which racial harassment and discrimination occurs. The purpose of this paper is to explore the discriminatory experiences and working conditions experienced by ethnic minority (EM) construction operatives in HK.

A mixed-method approach was adopted, including a questionnaire survey and focus group discussions. The survey identified the thoughts of EM construction workers about racial discrimination and harassment in the workplace. The focus group discussions were aimed at further exploring the discriminatory practices on HK construction sites and possible discrimination-coping strategies.

Questionnaire data from 100 EM site operatives and labourers mainly from Nepal and Pakistan, but some few from other Asian countries as well as, plus two focus group discussions suggested that indirect and subtle forms of racial harassment do exist on HK construction sites. The operatives sampled reported the existence of inequality of treatment in their working life. Communication difficulties caused by language barriers affect work relationships between different cultural groups on construction sites. EM site operatives tend to interact with workers of similar cultural and ethnic groups. On the corporate/company level, language support and translations of safety procedures notices and policies, should be established to bring staff together and promote a more inclusive and harmonious workplace.

The paper offers insights into the racial discrimination problems in the construction sector in an Asian context, which has been less explored. It aims to provide insight into the EM construction worker's situation in HK as well as the need for developing workplace-specific policies that protect against discrimination and protect the rights of EM workers.

Since the launch of the Mainland and Hong Kong Closer Economic Partnership Arrangement (CEPA) in 2003, Hong Kong cinema is believed to have confronted drastic changes. Hong Kong cinema is described to be dying, lacking creative space and losing local distinctiveness. A decade later, the rise of Hong Kong – China coproduction cinema under CEPA has been normalized and changed the once pessimism in the industry. The purpose of this paper is to demonstrate how Hong Kong cinema adjusted its production and creation in the first 10 years of CEPA.

Beginning with a review of the overall development, three paradigmatic cases are examined for reflecting upon what the major industrial and commercial concerns on the Hong Kong – China coproduction model are, and how such a coproduction model is not developed as smooth as what the Hong Kong filmmakers expected.

Collectively, this paper singles out the difficulties in operation and the limit of transnationality that occur in the Chinese context for the development of Hong Kong cinema under the Hong Kong – China coproduction model.

This is the author’s research in his five-year study of Hong Kong cinema and it contributes a lot to the field of cinema studies with relevant industrial and policy concern.