Construction Safety Research Articles

Flame spread characteristics of densified wood with various sample widths and densities

Densified wood (DW) is a novel functional material with great mechanical properties, its structural characteristics make it attractive as an ideal building material for mid-to-high-rise timber constructions. However, its flame spread characteristics are not yet fully understood, which threatens its further developments and applications. This study experimentally and analytically investigates the impact of density and width on the flame spread behaviors and heat transfer characteristics of DW. Flame spread rate, mass loss rate (MLR), and flame height decrease with increasing wood density. Both MLR and flame height increase with the increasing width, while flame spread rate increases first and then decreases. Dimensionless power-law relationships among them were established, which well predict the measurements. The dominant gaseous heat transfer transits from radiation to convection with the increase of density. The critical density for this transition is increased with width. Both width and density impact the preheating length and total heat flux, which are combined to influence the flame spread rate. A dimensionless correlation among them was established with an R2 of 0.915 to the experiments of current and previous publications. This work helps to understand flame spread of DW and benefits the fire safety in timber construction.

Application of a support vector regression model employing meta-heuristic algorithms for estimating pile bearing capacity

ABSTRACT The design of pile foundations hinges significantly on the pile-bearing capacity (PBC), a crucial factor affected by numerous soil features and parameters. Piles serve as a key component in relocating structural loads to the ground, underscoring the need for accurately determining PBC in geotechnical design. While prior research has delved into utilizing artificial neural networks (ANN) for predicting PBC, they encounter shortcomings such as grappling with finding global minima and slow convergence rates. This study introduces Support Vector Regression (SVR) as a machine-learning approach, augmented by two meta-heuristic optimization techniques: the Giant Trevally Optimizer (GTO) and Smell Agent Optimization (SAO), to achieve optimal results. These techniques empower engineers and data scientists to develop more precise and reliable predictive models in geotechnical engineering, ultimately enhancing the safety and efficiency of foundation design and construction. Drawing upon an extensive dataset compiled from previous studies, a predictive model was constructed to estimate PBC using soft computing techniques. The outcomes overwhelmingly favored the SVGT model, a fusion of the SVR model with GTO, demonstrating excellent anticipative capacities with an outstanding R2 value of 0.998 and an impressively low RMSE value of 91.21.

The Role of Universal Design in Enhancing Safety and Accessibility in Construction Sites in South-South Nigeria

The Role of Universal Design in Enhancing Safety and Accessibility in Construction Sites in South-South Nigeria

Effects of the excavation of deep foundation pits on an adjacent double-curved arch bridge

The excavation of deep foundation pits can cause variations in the displacement and stress fields of surrounding soils, which hence induces adverse effects on adjacent structures. This study presents a two-stage method to quantify the impact of the excavation of a deep foundation pit on the adjacent double-curved arch bridge in the historical city of Nanjing, Southeastern China. The entire process of the foundation pit excavation was simulated and the induced deformation of the arch foot was obtained in the first stage by hardening soil model with small-strain stiffness. Then, the obtained deformation of the arch foot was applied to the bridge structure as a displacement boundary in the second stage to calculate the internal forces and deformations of the double-curved arch bridge structure. The tensile strength of concrete is taken as the limit value of the tensile stress of the double-curved arch bridge. The limit values of arch foot displacement under four evaluation conditions are obtained by step loading calculation. The present results provide construction guidance and safety warning for the process of foundation pit excavation adjacent to double-curved arch bridges for historical preservation.

Sustainable Model for Improving Construction Industry Health and Safety Practices

Sustainable Model for Improving Construction Industry Health and Safety Practices

Automated construction safety reporting system integrating deep learning-based real-time advanced detection and visual question answering

The construction sector is globally acknowledged as one of the most hazardous industries, owing to the vulnerability of its workers to accidents, injuries, and even loss of life. Effective precautionary measures are necessary and ensuring the use of personal protective equipment (PPE) by workers is crucial for protecting them from accidents. Existing deep learning-based PPE detection systems mainly use simple vision-based target detection methods for tasks such as the identification of helmets or vests, and they tend to be task-specific. However, the identification of specific PPE based on respective job types and maintaining detailed safety records, requires further innovative approaches. In this paper, we propose an innovative intelligent system that not only accurately recognizes specific PPE according to the needs of different work types but also automatically generates safety inspection reports and establishes complete safety records, thus providing critical data to support accident investigations. The proposed system integrates a target detection model, visual question answering model, and text-based analysis of the relevant regulations to realize real-time detection of PPE and automatic generation of safety inspection reports. The experimental results show that the proposed YOLOv8n-DCA network strikes a good balance between performance and computational cost—, with a mAP value of 86%. Compared to the original YOLOv8n network, the mAP value is improved by 5.1%, while the model parameters and size are significantly reduced. Further, the visual question answering model exhibited a precision is 95.9. Finally, the automatic generation of safety inspection reports was successfully realized, verifying the feasibility of the developed system. This innovative system promises a comprehensive and efficient PPE management solution for the construction industry to ensure worker safety and provide strong data support.

Risk Identification and Safety Evaluation of Offshore Wind Power Submarine Cable Construction

To mitigate accidents in submarine cable construction within the rapidly expanding offshore wind power sector, this study employed the analytic hierarchy process (AHP) and risk matrix method (LS) to assess the risks associated with identified factors. Based on project research and expert consultations, five primary and twenty-two secondary risk factors were identified. AHP was utilized to rank the primary risk factors by severity, probability, and detection difficulty, with the highest risk being the environmental impact, followed by third-party destruction and worker error. LS was applied to rank the secondary risk factors by likelihood and severity, with the highest risks being complex submarine topography, low underwater visibility, and fishing operations. The study proposes risk reduction measures based on these evaluations and offers methodological guidance for improving construction safety in similar enterprises.

Dynamic Analysis and Temporal Governance of Safety Risks: Evidence from Underground Construction Accident Reports

Due to the complexity and dynamics of underground construction projects, safety risk management has experienced significant challenges restricting the sustainable development of underground space. The research on risk causal chains and risk coupling has yet to reveal the dynamic interactive characteristics of these risk factors and their temporal relationships over time. This study utilized a complex system view for safety risk analysis, using 37 accident investigation reports of underground construction projects. Combined with two novel and emerging analytical methods, temporal qualitative comparative analysis and crisp-set qualitative comparative analysis, this study discusses the temporal relationship of risk factors to the cause of accidents and explores the multi-actor coupling characteristics of management risk. The findings indicate that (1) compared with general construction projects, underground construction should pay more attention to management safety risks because they have an obvious time lag effect expressed in all accident causation paths, namely, preceding management risk, management risk, and machine/material risk cross-concurrently, and management risk initiation and (2) underground construction project management risks have three key main paths, namely, single-actor-dominated management deficiency (supervisors, owners, and subcontractors that cause management risks as a single-core actor) and dual-actor-dominated management deficiency (owner and subcontractor as dual core actors of management risk). Multi-actor-dominated management deficiency (owners, subcontractors, and supervisors are the multiple core actors of management risk). This study thus developed a temporal governance framework of underground construction safety risks based on the synergy of multi-actors and proposed risk governance strategies, such as synergistic multi-actor governance, to consider the temporal relationship of safety risk. This study further reveals the temporal and coupling characteristics of safety risks to enrich the risk casual chain theory and risk coupling theory and establish a systematic risk analysis framework for new guidance for safety and risk management for underground construction projects.

STUDY ON THE IMPACT OF DEFORMATIONS OF MULTI-STOREY BUILDINGS IN ALMATY

The article provides an assessment of the stability of buildings and structures in the city of Almaty under the influence of frequent earthquakes. Studying this process is not only interesting from a scientific point of view but also has great practical significance for ensuring the safety and stability of buildings in the city. The aim of the work is to investigate the condition of buildings in Almaty in terms of seismic safety characteristics. The research tasks include studying the types of buildings common in the city of Almaty and conducting stability calculations for buildings. This article selects the main and most common type of buildings and structures in each district of the city of Almaty as the research object. The calculations of the level of buildings' stability to seismic vibrations in different areas of the city, information on the estimated seismic resistance level of most buildings, and the calculation of their possible stability indicators are provided. The results show that the stability of buildings and structures in the city depends on reducing seismic activity from bottom to top based on their location (on the map). According to the calculation conclusions, it is necessary to increase the number of design organizations responsible for construction quality and use different building structures and construction methods in different parts of the city. Problems related to building deformation are relevant everywhere. Studying this process in Almaty will contribute to the global knowledge base and help create new technical solutions for safe construction.

Experiment Study on Rock Mass Classification Based on RCM-Equipped Sensors and Diversified Ensemble-Learning Model

The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification.

Prediction of the Nominal Side Resistance of Drilled Shafts in Dominantly Cohesive Soils using ANN

The conventional methods used in the design of drilled shafts might not fully consider the multiple sources of uncertainty in the soil such as the geometric and mechanical variability and/or the construction methods. These uncertainties can introduce nonlinearity to the analysis, leading to underestimation or overestimation of the resistance, which can be translated into expensive or even unsafe projects. Because of this, machine learning techniques such as artificial neural networks (ANNs), proven to be effective in solving nonlinear problems, are becoming popular in solving civil engineering problems. Therefore, the objective of this preliminary study is to evaluate a concept for predicting the nominal side resistance of drilled shafts with improved accuracy, using ANN. In this study, 45 load tests were collected from the extended version of the Nevada Deep Foundation Load Test Database and divided into 85% for training and 15% for testing. Then 1,638 ANN models were trained to determine the optimum model with a root-mean-squared error of 2,058 kips and an R-squared ( R2) of 85% on unseen load tests. The model was then benchmarked against the AASHTO predicted side resistance, with an average overall improvement of the prediction accuracy using ANN of 23%. This paper demonstrates that such models could be developed, improved, and used in the industry at an early stage and with limited data as supplemental tools that can help optimize designs in regard to construction safety, time, and cost.

Resilience Assessment of Deep Excavation Engineering Systems Based on Bayesian Network

As a key component of infrastructure, the construction safety of deep excavation is particularly important. In order to improve the safety and stability of Deep excavation construction, research on the risk resistance and rapid recovery capabilities of Deep excavation projects is carried out. By introducing the concept of resilience, we analyze the key factors that affect the safety and resilience of Deep excavation projects, use Bayesian networks to establish a resilience model that expresses the causal relationship between factors, and learn the parameters of the network model through data samples collected through surveys; by adjusting the network Analyze the relationship between each factor and system resilience at each node, and clarify the measures that should be taken and key control objects, thereby improving the system resilience and ensuring that normal construction can be carried out safely and orderly. Research shows that this model can be used to quantify the safety toughness value of deep excavation and reason and analyze the impact of various factors on the toughness value, thereby improving risk management and decision support capabilities.

Construction Safety Management of High-Rise Buildings under the Basic Principles of Disaster Sociology

In order to reduce the risk of accidents in high-rise building construction, firstly, the potential disaster risk factors in the construction of high-rise buildings were systematically analyzed, including the distortion of accident information transmission, the lack of morality during disasters, the danger of high-altitude work, the complexity of the intersection of types of work, and the non-professionalism of construction management. Secondly, this paper focuses on the analysis of the current problems in the construction safety management of high-rise buildings, and considers the social risk factors, including the distortion of safety accident transmission, the lack of morality during disasters and the disaster safety awareness of construction personnel. Finally, from the perspective of disaster sociology, specific improvement measures for the safety management of high-rise building construction are proposed. The results show that social risk factors are equally important in construction safety management, which not only enriches the theoretical basis of construction safety management of high-rise buildings, but also provides a specific theoretical reference for safety management in practice.

Assessing proxemics impact on Human-Robot collaboration safety in construction: A virtual reality study with four-legged robots

Recent advancements in four-legged robots have prompted their integration into the construction industry, yet the safety implications of their deployment remain inadequately explored. As such comprehensive investigations are required to ensure the safety of robot deployment and the well-being of construction professionals who work with and alongside these robots. This study addresses this gap by conducting a user-centered experiment employing virtual reality to assess human behavior and safety impacts in varying interaction spaces with four-legged robots within a simulated construction environment. By employing objective and subjective measures, including physiological and attentional responses, emotional reactions, situational awareness, risk perceptions, and attitudes towards robots, this study analyzes the impact of proxemics on construction individuals at two distinct interaction spaces: proximal (1.5 – 4 ft) and distal (12 – 25 ft) from the four-legged robots. The study found that while participants’ physiological responses, emotional states, situational awareness, risk perceptions, and attitudes towards robots were not significantly influenced by four-legged robot interaction space, those in the distal group allocated significantly more attention to the robot, particularly in terms of fixation count, indicating a significant proxemics impact on attentional states. These findings shed light on the safety implications of human-robot collaboration on jobsites, contributing to the advancement of safe and efficient practices in construction settings.

Research Progress on the Mechanisms and Control Methods of Rockbursts under Water–Rock Interactions

Rock bursts are among the most severe and unpredictable hazards encountered in deep rock engineering, posing substantial threats to both construction safety and project progress. This study provides a comprehensive investigation into how moisture infiltration influences the propensity for rock bursts, aiming to establish new theoretical foundations and practical methods for their prevention. Through the analysis of meticulous laboratory mechanical experiments and sophisticated numerical simulations, we analyzed the variations in the physical and mechanical properties of rocks under different moisture conditions, with a particular focus on strength, brittleness, and energy release characteristics. The findings reveal that moisture infiltration significantly diminishes rock strength and reduces the likelihood of brittle fractures, thereby effectively mitigating the risk of rock bursts. Additionally, further research indicates that in high-moisture environments, the marked reduction in rock burst tendency is attributed to increased rock toughness and the suppression of crack propagation. This study advocates for the implementation of moisture control measures as a pre-treatment strategy for deep rock masses. This innovative approach presents a viable and effective solution to enhance engineering safety and improve construction efficiency, offering a practical method for managing rock burst risks in challenging environments.

PageRank Algorithm-Based Recommendation System for Construction Safety Guidelines

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on “High-rise Building Construction (General) Safety and Health Guidelines”, using ‘scaffolding’ as the keyword, demonstrates the framework’s effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as ‘fall’, ‘drop’, and ‘scaffolding’, with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management.

Dynamic spatial–temporal conflict quantification of construction machinery for pouring blocks in arch dams

PurposeThe spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).Design/methodology/approachFirst, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.FindingsThe quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.Originality/valueThis study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.

Assessing fragmentation and potential sliding zones in rock tunnels via computer vision technology

During the excavation of rock tunnels, accurately understanding the structural characteristics of the tunnel face is crucial for ensuring construction safety. The study evaluates the structural characteristics of the tunnel face by precisely characterizing and analyzing parameters like fracture trace length and dip angle, aiming to calculate the degree of fragmentation and potential sliding zones of the rock mass. In evaluating rock fragmentation degree, the study identifies and quantifies fracture trace lengths as a single-factor indicator to assess the fragmentation of the rock mass on the working face. By comparing with the p21 index, the reliability and reasonableness of the rock fragmentation evaluation are discussed. For the evaluation of potential sliding zones, a method for approximating the extraction of fracture traces is proposed. The assessment is then conducted based on multi-factor indicators, including fracture dip angle and length. Additionally, the advantages and disadvantages of various indicators under different calculation methods are discussed. The study finds that rock fragmentation indicators offer a more detailed and accurate description of the actual fracture density of the rock mass compared to the P21 index, proving to be generally more reliable under extreme conditions. Furthermore, the evaluation of rock fragmentation and potential sliding zones on the rock face can provide important references for the refined extraction of rock mass structural characteristics, ensuring the safe construction of tunnels.

Status of Construction Safety in Local Road Bridge Construction and its Consequences in Project Implementation: A Study in Gandaki Province, Nepal

After federalization of Nepal, bridge construction in local roads by provincial levels is in increasing trend. Being highly technical and of risky nature, construction safety needs to be ensured in bridge construction. However, in countries like Nepal, safety issues are considered, with follow up measures, only after the accidents occur at construction sites. In this context, this study aims to assess the present status and consequences of construction safety in local roads bridge project implementation and explore some improvements measures. A quantitative descriptive design was adopted for the study and 30 completed bridge samples from Gandaki province were selected using convenience sampling. Data were collected using questionnaire survey with 56 participants directly related to bridge construction and policy implementation. This study revealed that most of the bridge construction sites have poor workplace safety and worker’s welfare, inadequate provision and use of PPE, lack of trainings and awareness to workers as well as the absence of safety plans and method statements. Similarly, provisions for unsafe supports, shoring, formworks, concreting, and demolition were not noticed. While the absence of safety supervisor, job safety analysis, recording and reporting accident and diseases was observed, abundant cases were found where less importance was given to construction safety and use of PPE undermining the engineering and administrative hazard control methods in road bridge construction. Slip/trip/low falls, hand/leg/head injury, falling from height, eye problem, injury during erections, vehicle and electric current, cost and time overrun, substandard quality, reduced workers’ productivity, increased workers turnover, low construction safety reputation of contractor, loss/damage of land, irrigation canal, drinking water scheme, dust/noise have been found as consequences of poor safety practices. This clearly indicates that present bridge construction safety practices are inadequate and need substantial improvements for better project implementation. The major areas of improvements include safety as first priority, willingness, awareness and commitment of management, contractor and worker, selection of competent workers, formulation of specific safety rules, regulations, codes and their strict enforcement, good communication, safety training and inspection, motivation, adequate budget provision in contract for safety, provision of PPE, feeling safety as self-responsibility and use of technologies as well.

Influence of fly ash and granulated blast furnace slag on autogenous shrinkage and drying shrinkage of cement-based materials mixed with alkali accelerator and alkali-free accelerator

Shotcrete shrinkage and cracking are getting more threatening due to its high content of cementitious materials, small aggregate particle size and a large amount of accelerator, which poses a great threat to the construction safety of engineering structures. This paper studies the volume stability of cement mortar with different contents of fly ash (FA) and granulated blast furnace slag (GBFS) (10%/20%/30%) under the condition that the dosage of alkali liquid accelerator is 4% and that of the alkali-free liquid accelerator is 7%. This work is designed to provide a guidance for the basic research and practical application. By comparing the autogenous shrinkage, drying shrinkage, mass loss caused by drying condition, and internal humidity change of drying condition of cement mortar within 180 days of age. Combined with MIP analysis, XRD hydration product analysis, and SEM hydration product morphology analysis, the results show that both fly ash and granulated blast furnace slag can reduce the autogenous shrinkage and drying shrinkage of cement mortar mixed with accelerator to a certain extent, and the shrinkage reduction effect of FA is more prominent. The reduction effect of GBFS is more obvious at high dosages. Adding FA and high content of GBFS can increase the mass loss of cement mortar under drying shrinkage conditions. The high content of mineral admixtures will exacerbate the rate of decrease in the internal humidity of the cement mortar. The reasons for the reduction of autogenous shrinkage and drying shrinkage of cement mortar mixed with accelerators due to the addition of FA and GBFS are: the early activity of the two mineral admixtures is low, and the hydration process is slow. And the reduction of pore size with the range of 5-50nm leads to the reduction of capillary tensile stress and the weakening of the driving force for shrinkage deformation. Among them, the shrinkage reduction effect of fly ash is more pronounced. Therefore, mineral admixture can be used to replace a certain amount of cement in actual shotcrete construction, so as to increase the volume stability of shotcrete.