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The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

This opinion piece draws on the author's experience as a thought leader and expert practitioner in risk management to explore possible routes to applying antifragility in the organisational context, drawing on three metaphors from outside the business domain. Organisational responses to stressors have focused on the development of robustness and resilience. Recent global events have highlighted weaknesses in both these approaches. Antifragility might prove to be a valuable addition to the organisational armoury, but little progress has been made in finding practical implementations of the concept since it was first proposed over 10 years ago (Taleb, 2012).

Distinctions between robustness, resilience and antifragility are clarified. Descriptive analogy is used to expose ways in which antifragility might be implemented in practice, by comparison with three disparate metaphors.

Antifragility is currently not well understood or implemented, but it offers a potentially powerful additional organisational strategy in response to stress, to complement more traditional robustness and resilience approaches. Drawing on the three metaphors, four distinct types of antifragility are outlined which suggest how organisations might begin to develop antifragility in practice: innate antifragility, adaptive antifragility, rheopectic antifragility and emergent antifragility. These are presented as an organisational antifragility taxonomy that can support further research and practice.

The use of metaphor to explore antifragility is unique, providing insights into ways it might be applied.

The purpose of this paper is to present an applied method to design the low-speed contact between a mass and surface of a beam using an analytical solution based on the first-order shear deformation beam theory. Also, a simulation of impact process is carried out by ABAQUS finite element (FE) code.

In theoretical formulation, first strains and stresses are obtained, then kinetic and potential energies are written, and using a combination of Ritz and Lagrange methods, a set of system of motion equations in the form of mass, stiffness and force matrices is obtained. Finally, the motion equations are solved using Runge–Kutta fourth order method.

The von Mises stress contours at the impact point and contact force from the ABAQUS simulation are illustrated and it is revealed that the theoretical solution is in good agreement with the FE code. The effect of changes in projectile speed, projectile diameter and projectile mass on the results is carefully examined with particular attention to evaluate histories of the impact force and beam recess. One of the important results is that changes in projectile speed have a greater effect on the results than changes in projectile diameter, and also changes in projectile mass have the least effect.

This paper presents a combination of methods of energy, Ritz and Lagrange and also FE code to simulate the problem of sandwich beams under low velocity impact.

Technologies have had a positive impact on the construction industry. Technologies such as BIM, automation, augmented and virtual reality, Internet of Things and robotics have been adopted by construction firms to enhance productivity. However, not much research has been done on the awareness and adoption of wearable technologies for health and safety (H&S) management. This paper investigates the level of awareness and adoption of wearable technologies for H&S management in the Nigerian construction industry.

A quantitative research method was adopted for the study. An electronic questionnaire format was used as an instrument to collect the data. Both descriptive (mean score) and inferential statistics (Kruskal–Wallis test) were used to analyse the data.

The results indicate that organisations rarely use H&S wearable devices for H&S management although professionals within the construction industry are somewhat aware of the common H&S wearable devices. The findings further indicate that all 11 variables were perceived as “rarely adopted”, whereas 2 variables were perceived as “aware”, 3 variables as “slightly aware” and the remaining 6 variables as “somewhat aware”.

Data were collected from only construction professionals working in government agencies, consultancy firms and grade D contracting firms in Lagos and Abuja. For a broader perspective, a study that expands the number of states and categories of construction firms is recommended.

The construction industry in Nigeria can use the recommendations to improve H&S management on site. Moreover, the recommendations can contribute to the development of policies to promote the adoption of wearable technologies in construction sites.

Research on wearable technologies, particularly in the Nigerian construction industry, is at the developing stage. With this article, the authors contribute to the body of knowledge in this area of research.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

Construction safety is heavily affected by using new technologies in this growing trend of technology adoption. Especially, safety performance is enhanced through the utilization of some effective technologies such as artificial intelligence, virtual reality, BIM and wearable devices. Therefore, the main purpose of this study is to investigate the influence of emerging technologies on construction safety performance and quantify the relationship between those. The proposed components of emerging technologies are BIM, GIS, VR, RFID, AI, ML, eye tracking and serious games and wearable devices, whereas the dimensions of construction safety performance are safety planning, safety training, safety inspection and monitoring, safety audits and reviews and safety leadership.

A structural model was composed consisting of emerging technologies and safety performance indicators. Then, a questionnaire was designed and administered to construction professionals, and data from 167 projects were analyzed using structural equation modeling. The data were analyzed by using software, called SPSS AMOS.

The analysis of the structural model proves that there is a positive and significant relationship between emerging technologies and construction safety performance. Moreover, the factor loadings for each factor were found to be high indicating a good representation of the construct by the components developed. Among the technologies, BIM, robotics and automation, AI and wearable devices were detected to be the most significant technologies in terms of impacting safety performance.

The study contributes to the body of knowledge in that it develops a conceptual framework consisting of specific technologies in terms of emerging technologies, reveals the impact of such technologies on safety performance and proposes several tools and strategies for enabling effective safety management along the project lifecycle. Industry practitioners may benefit from the framework developed by adopting such technologies to enhance their safety performance on construction projects.

High rate of accidents continue to plague the construction industry. The advancements in safety technologies can ameliorate construction health and safety (H&S). This paper aims to explore the use of emerging technologies as an effective solution for improving safety in construction projects.

Following a detailed literature review, a questionnaire survey was developed encompassing ten technologies for safety management and ten safety enablers using technologies in construction. A total of 133 responses were gathered from Malaysian construction practitioners. The collected quantitative data were subjected to descriptive and inferential statistical analyses to determine the meaningful relationships between the variables.

Findings revealed that the most effective emerging technologies for safety management are: building information modelling (BIM), wearable safety technologies and robotics and automation (R&A). The leading safety enablers are related to improve hazard identification, reinforce safety planning, enhance safety inspection, enhance safety monitoring and supervision and raise safety awareness.

Safety is immensely essential in transforming the construction industry into a robustly developed industry with high safety and quality standards. The adoption of safety technologies in construction projects can drive the industry towards the path of Construction 4.0.

The construction industry has historically been slow to adopt new technology. This study contributes to advancing the body of knowledge in the area of incorporating emerging technologies to further construction safety science and management in the context of the developing world. By taking cognisance of the pertinent emerging technologies for safety management and the safety enablers involved, construction safety can be enhanced using integrated technological solutions.

This study aims to develop a safe, wearable clothing system that combines visibility-enhancing and emergency–accident-responding functions for two-wheeled vehicle (TWV) users' safety assistance.

First, the wearable system (WS) allowing users to control turn signals, brake lights and emergency flasher only with head movements was developed. Second, multiconnected systems were developed between WSs and a smartphone application (AS), providing accident occurrence recognition, driving photo capture–storage and emergency notification functions. Third, usability testing in each function was performed to assess the operability of the systems.

The intuitive interface, which uses head movement as gesture commands, was effectively operated for controlling turn signals, brake lights and emergency flasher when driving, despite differences in user physique and boarding structure among TWVs. In addition, using Bluetooth low energy and Wi-Fi protocols simultaneously can establish automatic accident recognition–notification and driving photo capture–storage–display functions by linking two WSs with one AS.

This study presents a case using relatively accessible technologies within the fashion industry to improve users' safety and provide fundamental data for convergence education for smart fashion products, highlighting the significance of this study in this convergence era.

The WSs and the AS of a TWV user visually evoke the attention of other drivers and pedestrians, reducing the risk of accidents; social contribution regarding public safety will be possible by allowing the system to autonomously inform emergencies and receive emergency medical treatment quickly when the accident occurred.

Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

The construction industry remains one of the most hazardous industries worldwide, with a higher number of fatalities and injuries each year. The safety and well-being of workers at a job site are paramount as they face both immediate and long-term risks such as falls and musculoskeletal disorders. To mitigate these dangers, sensor-based technologies have emerged as a crucial tool to promote the safety and well-being of workers on site. The implementation of real-time sensor data-driven monitoring tools can greatly benefit the construction industry by enabling the early identification and prevention of potential construction accidents. This study aims to explore the innovative method of prototype development regarding a safety monitoring system in the form of smart personal protective equipment (PPE) by taking advantage of the recent advances in wearable technology and cloud computing.

The proposed smart construction safety system has been meticulously crafted to seamlessly integrate with conventional safety gear, such as gloves and vests, to continuously monitor construction sites for potential hazards. This state-of-the-art system is primarily geared towards mitigating musculoskeletal disorders and preventing workers from inadvertently entering high-risk zones where falls or exposure to extreme temperatures could occur. The wearables were introduced through the proposed system in a non-intrusive manner where the safety vest and gloves were chosen as the base for the PPE as almost every construction worker would be required to wear them on site. Sensors were integrated into the PPE, and a smartphone application which is called SOTER was developed to view and interact with collected data. This study discusses the method and process of smart PPE system design and development process in software and hardware aspects.

This research study posits a smart system for PPE that utilises real-time sensor data collection to improve worksite safety and promote worker well-being. The study outlines the development process of a prototype that records crucial real-time data such as worker location, altitude, temperature and hand pressure while handling various construction objects. The collected data are automatically uploaded to a cloud service, allowing supervisors to monitor it through a user-friendly smartphone application. The worker tracking ability with the smart PPE can help to alleviate the identified issues by functioning as an active warning system to the construction safety management team. It is steadily evident that the proposed smart PPE system can be utilised by the respective industry practitioners to ensure the workers' safety and well-being at construction sites through monitoring of the workers with real-time sensor data.

The proposed smart PPE system assists in reducing the safety risks posed by hazardous environments as well as preventing a certain degree of musculoskeletal problems for workers. Ultimately, the current study unveils that the construction industry can utilise cloud computing services in conjunction with smart PPE to take advantage of the recent advances in novel technological avenues and bring construction safety management to a new level. The study significantly contributes to the prevailing knowledge of construction safety management in terms of applying sensor-based technologies in upskilling construction workers' safety in terms of real-time safety monitoring and safety knowledge sharing.