Construction Site Research Articles

Prevalence of occupational injuries among construction workers in Karachi, Pakistan.

The construction industry in Pakistan employs 8% of the labour force, contributing 3% to the national GDP. This study aimed to determine the prevalence and associated factors of occupational injuries among construction workers in Karachi. We conducted a cross-sectional survey among 448 men from 10 construction sites in Karachi between June and October 2022. Workers aged ≥18 years involved in masonry, cleaning, machine operating, and supervision tasks were recruited and completed a structured, interviewer-administered questionnaire. Occupational injury was defined as "any injury at work over the last year for which the worker sought medical care or took time off work". We found the prevalence of occupational injury to be 26% (n = 116). Workers from small construction were more likely to report injuries, with an OR of 2.01 (95% CI: 1.30-3.12). Additionally, those working more than 8 hours daily had greater odds of injury, with an OR of 2.30 (95% CI: 1.45-3.67). This study found a high prevalence of injuries among construction workers in Karachi, Pakistan, emphasizing the need for preventive measures and interventions for improving health and safety at construction sites.

Research on Deepening and Optimizing the Special Plan for Deep Excavation Based on Subway Engineering Site Information

This article aims to optimize the safety measures of the special plan and proposes a Bayesian network method for modeling the safety risks of construction sites based on construction site information. It transforms the optimization problem of safety measures into a dynamic combination problem of safety measures and introduces a genetic algorithm to solve this combination optimization problem. Improving the safety level of the subway construction site has reduced the construction risk factors.

IMPERMEABILIZAÇÃO COM ARGAMASSA POLIMÉRICA, EM UMA CONSTRUÇÃO CIVIL EM MANAUS – ESTUDO DE CASO.

ABSTRACT Mortar is one of the main products on the current market that brings innovations with the aim of improving technical production performance, avoiding waste and prioritizing sustainability in each action, minimizing environmental impacts. Therefore, this study aims to evaluate waterproofing with polymer mortar to ensure effective protection of structures against the penetration of water and humidity. The methodology used in this research consists of a bibliographical research that is based on the work of other authors on the topic, carried out through consultation in scientific articles and theses, related to the topic and a field research in a construction site, in a neighborhood of Manaus-Amazons. As a result, the study revealed that waterproofing with polymer mortar is a process that uses a mixture of cement, sand and polymers to create an impermeable layer on surfaces, preventing the passage of water and humidity. This mortar forms a protective barrier, especially in areas that are constantly exposed to water. In other words, it is a practical solution, as it usually comes in the form of a powder or paste that is mixed with water for application. Keywords: Waterproofing; Mortar; Civil Engineering; Amazon

MODFLOW-based groundwater level prediction and comprehensive determination of anti-floating water level for airports under construction

The fluctuation of the groundwater level at a construction site is crucial in determining the design of the anti-floating water level. The design of the anti-floating water level is mainly based on the water level measured during the survey period combined with the information acquired from surrounding engineering. However, this approach fails to predict the future variation of groundwater level, which can potentially endanger the safety during the construction process and building occupancy. Therefore, a comprehensive method for determining the anti-floating water level is proposed based on the research background of expanding the airport site in Jinan. In conjunction with the actual survey of the expanded airport, numerical simulation research is conducted to predict the anti-floating water level under groundwater changes at three different depths of 3, 8, and 16 m. The anti-floating water levels corresponding to the depths are 19.2–18.3 m, 19.1–18.3 m, and 18.9 m−18.1 m, respectively. The analysis of the changes in groundwater level and precipitation in the site reveals a linear relationship between groundwater level and precipitation during January 2018 to December 2022. In addition, a quantitative theoretical calculation method for anti-floating water levels is proposed. Finally, a comprehensive comparison is performed between the anti-floating water level based on groundwater level and rainfall data and the simulated anti-floating water level. The measured and simulated anti-floating water levels are essentially the same in the western part of the study area, while the anti-floating water level obtained from the numerical simulation is lower in the southeast and higher in the northeast region. On this basis, the highest value among the measured and simulated is taken, and the anti-floating water level of the site is divided. The anti-floating measurements are proposed.

Real- Time Worker’s Safety Monitoring On Scaffolding Using Vision Based System

The construction industry presents its problems regarding safety maintenance, particularly in matters of worker falls. In this case, falls are projected to grow from 33-38%. This project intends to reduce these dangers by developing a vision-based real-time worker’s safety monitoring system that ensures proper use of Personal Fall Arrest Systems (PFAS) on scaffolding. PFAS patients must wear helmets, harnesses, and lanyards because their proper placement is critical in avoiding injury due to falls. The methodology starts with collecting an image dataset. Every image is painstakingly labelled to show all instances of the specific PFAS components and their relevant states. Normalization is performed to match the standards of image inputs with the selected object detection algorithm, YOLOv11. The dataset is divided into training, validation, and testing units, which is representative of the actual situation. This guarantees effective training and testing of the YOLOv11 model for reliable and accurate real-time detection. The verification tests are conducted using the F1 score metric, which evaluates the model in a holistic way. After configuration, the trained YOLOv11 model is deployed on Open CV in the Visual Studio Code (VS Code) environment to enable real-time detection. The ultimate objective is to deliver a system that significantly reduces fall accidents, enhances worker safety, and facilitates compliance with stringent construction site safety standards.

Failure of multistorey parking site in hilly region and its forensic geotechnical investigation

A multi-storey car parking was proposed on a sloping ground in the hilly region of an Indian state, which started showing distress during the initial stages of the construction. The RCC wall, which was integrated with the framed structure and constructed for the purpose of supporting the excavated earth material at the construction site, initially deformed and then finally failed after the heavy rainfall in the region. Forensic Geotechnical investigation (FGE) was carried out in the light of IS:1904, IS:6403, IS:3764 and IS:14,243 (Part2) as well as with the help of numerical analysis using FEM based tool PLAXIS 2D. The outcome of the FGE revealed shortcomings pertaining to (i) improper geotechnical subsurface investigations up to the desired depth as well as estimation of Safe Bearing Capacity (SBC), (ii) site development activity carried out without taking appropriate measures for slope protection works and (iii) insufficient drainage facilities, (iv) loading the frame structure without connecting columns with tie beams. Finally, recommendations and suggestions on various ground improvement techniques, such as slope protection measures using soil-nailing or constructing a retaining wall to protect the nearby properties; retrofitting of constructed foundation using micro-piles, providing proper drainage facilities, etc., are made to protect the site before the resumption of the work.

BARRIERS AND SOLUTIONS OF BUILDING INFORMATION MODELLING (BIM) IN CONSTRUCTION SITE SAFETY IN MALAYSIA

The construction industry faces a high fatality rate due to its hazardous work environment and the inherent risks associated with construction activities. These challenges results in numerous incidents, injuries, and illnesses among workers, highlighting the urgent need for enhanced safety measures. Building Information Modelling (BIM) has shown considerable promise in improving safety on construction sites. This study explores the challenges of adopting BIM for construction site safety and suggests potential solutions within the Klang Valley, Malaysia. A questionnaire survey was conducted to evaluate ten major barriers and ten corresponding solutions to BIM adoption. The survey targeted 250 construction professionals with BIM experience, and the collected data were analysed using descriptive statistics in SPSS Version 27. The findings identified interoperability limitations, resistance to change, and lack of knowledge and skills as the primary barriers to adopting BIM for construction site safety. On the other hand, appointing a BIM Safety Manager or Coordinator, integrating BIM with real-time monitoring technology, and enhancing safety planning and visualisation through BIM were identified as the top solutions for implementing BIM in building projects within the Klang Valley.

CHALLENGES AND PROSPECTS OF IMPLEMENTING AUTOMATED SYSTEMS IN THE CONSTRUCTION INDUSTRY

In the context of rapid technological advancements and increasing demands for enhanced efficiency, quality, and safety in construction, the implementation of robotics and automation on construction sites has become a promising direction. The use of robots and automated systems enables a wide range of tasks, from preparing the foundation for construction to assembling structural elements, thereby opening new horizons for the development of residential and industrial complexes, optimizing production processes, and significantly reducing time costs. Automation minimizes the impact of the human factor, which is one of the key causes of errors in construction [1]. Utilizing robots enhances the accuracy of tasks outlined in the plan while ensuring compliance with modern regulatory standards. This research focuses on analyzing the key prospects, challenges, and technological solutions associated with the implementation of automated systems in the construction industry. It explores innovative approaches to automation, the impact of emerging technologies on construction activities, and the potential use of autonomous systems in the development of residential complexes. The proposed conclusions demonstrate that integrating automated systems is not only an effective tool for optimizing time and resource expenditures but also a powerful driver of sustainable development in the construction industry. Moreover, implementing automated solutions at the state level allows not only keeping up with modern trends but also establishing new standards of efficiency, quality, and safety in construction.

Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model

Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model

Promoting Sustainable Water and Sanitation Practices on Construction Sites Using Bio-Digester Technology: Advancing Progress toward SDG 6

The challenge of ensuring proper sanitation on construction sites, particularly in remote and infrastructure-intensive regions of India, necessitates sustainable and innovative solutions. This research explores the implementation and effectiveness of bio-digester septic tanks infused with DRDO-developed anaerobic microbial inoculum as an alternative to traditional septic systems in labor camps associated with large-scale construction projects. Conducted at the Mindhola River bridge project site in Gujarat, the study compares conventional septic systems with bio-digesters in terms of installation time, cost-effectiveness, maintenance requirements, environmental impact, and safety. Water and soil quality parameters—including pH, turbidity, TDS, TSS, BOD, COD, and coliform presence—were analyzed, showing that bio-digesters outperform traditional systems in almost all aspects. Additionally, the use of bio-enzymes prepared from kitchen waste was tested as an environmentally friendly cleaning agent, proving effective in maintaining microbial balance and reducing chemical dependency. The bio-digester systems were not only quicker to install and more mobile but also reduced construction-related hazards and offered better long-term utility. These findings support the potential of bio-digesters as a viable sanitation solution aligned with Sustainable Development Goal 6 (Clean Water and Sanitation), especially in underserved or remote project areas. The research highlights the need for policy-level support, industry-wide adoption, and awareness regarding the benefits of integrating such sustainable technologies in infrastructure projects.

Pemetaan Potensi Wilayah Untuk Pembangunan Observatorium di Jawa Timur Menggunakan Software ArcGIS

East Java Province has diverse geographical conditions and several areas with low levels of light pollution, making it a potential location for the construction of an observatory. This study aims to map these potential areas using ArcGIS software. The research is a library research with a qualitative descriptive approach. The data used include light pollution data from the Light Pollution Maps (LPM) website, wind speed and cloud data from the Weather Spark website, elevation data from Google Earth, and topographic data from the Digital Elevation Model (DEM). Data collection was carried out through literature study and then analyzed using a descriptive method to identify areas that meet the criteria for observatory development. The results show that East Java has varied elevation levels and several regions with low light pollution. Based on the analysis, three main locations were identified as potential sites for observatory construction: Bromo National Park in Lumajang Regency, Ijen National Park in Bondowoso Regency, and Meru Betiri National Park in Jember Regency. These three locations meet the ideal criteria for an observatory, such as sufficient elevation, minimal light pollution, sparse settlements, and relatively stable climatic conditions.

Research on remaining useful life prediction method for bucket elevator bearings based on multi-module adversarial domain generation

Abstract Predicting the remaining useful life (RUL) of construction site bucket elevators using deep learning faces significant challenges due to variations in working conditions, which lead to data distribution divergences and limit the applicability of traditional transfer learning methods. To overcome these challenges, this study presents the Multi-module Adversarial Regularization and Synthesis for Domain Generalization with Domain-Adversarial Neural
Network (MARS-DG-DANN) framework. The framework integrates a time series predictive auxiliary and regularization model for feature extraction, a deep adversarial generative model to enhance domain generalization, and a multi-source domain adaptation technique for handling augmented domains. It further incorporates innovative methods, including a lossless spiral matrix signal transformation and an alternating training strategy for the generative model. Validation experiments conducted across three distinct conditions demonstrated the robustness and accuracy of the proposed framework in RUL prediction. By effectively addressing domain shifts, the MARS-DG-DANN framework significantly enhances the generalization performance of predictive models, providing a reliable solution for applications under diverse working conditions.

Construction Site Incident Reporting Automation Using Natural Language Processing

In the construction sector, effective communication about incidents is important for improving safety policies and procedures. Lately, technological advancements in construction have made automating document preparation within an information system possible. However, it requires a great deal of time and effort for a human operator to prepare such documents manually, which in most cases leads to inaccuracies. This paper proposes a new design and development of construction site incident reporting systems based on the automation of event reporting using Natural Language Processing (NLP). Its purpose is to increase reporting productivity, accuracy, and the level of compliance with relevant statutes and regulations. Worksite mishaps continue to challenge both human welfare and economic resilience. Reports from global organizations indicate an upward trend in mishap frequency and critical consequences. Traditional techniques depend heavily on handwritten entries, causing postponements and obstructing immediate evaluations. This research introduces a smart reporting mechanism utilizing NLP, aimed at simplifying the notification workflow. The platform incorporates voice-to-text conversion to interpret verbal inputs from personnel, followed by NLP-driven contextual interpretation and essential data extraction. The background study reveals significant capabilities of NLP in advancing safety strategies, pointing to strengths like better reliability and shortened notification durations. The system includes information acquisition, machine learning for audio processing, and NLP-based detail identification. Predefined digital formats support fast and consistent reporting, reinforcing job site precautionary measures and preventing recurrence of hazardous events. By boosting the speed and reliability of issue communication, the model strives to refine safety procedures and assist in thorough incident analysis.

The Impact of Work Sequence-Based Safety Training on Workers’ Cognitive Effectiveness at Construction Sites

Daily pre-work safety training for workers is essential due to their exposure to various hazardous factors. However, existing training methods often fail to effectively deliver information in a way that supports workers’ learning and comprehension, resulting in poor job performance and a lack of adherence to safety protocols. This study explores a novel approach to structuring safety training by examining the cognitive characteristics of workers in relation to the placement order and composition of training materials. An educational recall experiment involving 660 construction workers was conducted to evaluate the impact of training material design on content retention. The results showed that the average number of recalls was significantly higher when the training content was structured according to the actual work sequence, compared to content organized in a random order. Additionally, separating execution standards from safety standards led to better recall than when the two were integrated. These findings highlight the importance of aligning training materials with workers’ cognitive processing patterns. This study contributes to both theory and practice by providing evidence-based guidance for improving pre-work safety training. The proposed method offers a practical solution for small- and medium-sized construction companies that often lack the resources to develop effective training materials.

Assessment of water quality in the piped water supply system by using Water Quality index method

Background Drinking water of the right and approved quality is a basic requirement for the development of any civilization. According to SDG 6 it is crucial to provide every citizen with equitable water quality and quantity. Methods The study area is the smart city of Bhubaneswar with 67 wards and three zones: the north zone, south-west zone, and south-east zone with around 12.4 lakhs population. To maintain the water quality in the supply pipeline in this city, which is always assumed to be safe, it needs to be examined within certain time intervals to check the contamination. In this context, studies on water quality parameters in the supply pipeline network from different anticipated vulnerable areas have been collected for testing. In this context, water samples were collected from areas near industries, market complexes, educational institutions, and construction sites of each ward of Bhubaneswar. Nearly 10 water quality parameters were tested and analyzed using the weighted arithmetic water quality index method. This method takes input of all the parameters and provides the overall water quality index value, which classifies the water in different grades like excellent, good, poor, and very poor quality. Results According to the WHO (2011) guidelines, the study found that there is a deviation in the parameters like 10.78% in pH, 19.48% in dissolved oxygen, 43.88% in conductivity, and 22.95% in hardness from the standard limit, but the overall water quality index indicates the water is not in the poor and very poor range. Also, water quality index values identify that the water in the south-east zone is excellent compared to the north and south-west zones. The reason for the slight deterioration is due to the underground cable works, road works and also some areas where the old pipeline system is still existing due to personal encroachment of people as per Public health Engineering department.

Review of AGV Obstacle Avoidance Algorithm Based on Machine Vision

With the development trend of unmanned and intelligent, it has become a development trend for AGV to leave human operators and perform diversified tasks. However, operating in complex environments such as urban streets, mines, and construction sites is a difficult problem that needs to be solved currently. Obstacles in complex environments pose a serious threat to the AGV trolley in operation. Obstacle avoidance technology has become a key part of the AGV trolley’s task decision-making system and plays an important role in ensuring the safe operation of the AGV trolley and improving work efficiency. This article first elaborates on the concept of the AGV trolley obstacle avoidance algorithm and the evaluation criteria for the optimal path, then elaborates and compares the advantages and disadvantages of different types of obstacle avoidance algorithms such as optimization-based, potential field-based, and machine learning-based obstacle avoidance algorithms, and finally obtains the research focus and direction of the AGV trolley.

An Image-Based Intelligent System for Addressing Risk in Construction Site Safety Monitoring Within Civil Engineering Projects

In the construction industry, safety is of paramount importance given the complex and dynamic nature of construction sites, which are prone to various hazards, like falls from heights, being hit by falling objects, and structural collapses. Traditional safety management strategies, such as manual inspections and safety training, have shown significant limitations. This study presents an intelligent monitoring and analysis system for construction site safety based on an image dataset. A specifically designed Construction Site Safety Image Dataset, comprising 10 distinct classes of objects, is utilized and divided into training, validation, and test subsets. InceptionV3 and MobileNetV2 are chosen as pre-trained models for feature extraction and are modified through truncation and compression to better suit the task. A novel feature fusion architecture is introduced, integrating these modified models, along with a Squeeze-and-Excitation block. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 0.90 at an IoU threshold of 0.5, with high accuracies for classes like “Safety Cone” (91%) and “Machinery” (93%) but relatively lower accuracy for “Vehicle” (57%). The training process exhibits smooth convergence, and compared to prior methods, such as YOLOv4 and SSD, the proposed framework shows superiority in regard to precision and recall. Despite its achievements, the system has limitations, including reliance on visual data and dataset imbalance. Future research directions involve incorporating multi-modal data, conducting real-world deployments, and optimizing for edge deployment, aiming to further enhance construction site safety.

Safety Management Plan for New Construction Power Train Assembly

Construction work involves various hazards that pose significant risks to workers, equipment, and the surrounding environment. The primary objective of this Safety Manual is to provide a comprehensive framework for preventing accidents, minimizing injuries, and ensuring a safe work environment on construction sites. The manual outlines essential safety protocols, hazard identification processes, risk assessments, and best practices for workers, supervisors, and contractors. Key topics covered include personal protective equipment (PPE), fall protection, electrical safety, scaffolding, machinery operation, confined space entry, and the handling of hazardous materials. Additionally, the manual emphasizes the importance of training, emergency response procedures, and continuous monitoring of safety standards. The guidelines within this manual are intended to comply with local and national safety regulations, ensuring that construction sites maintain high safety standards and contribute to the well-being of all personnel involved. The fostering a safety-first culture, this manual aims to reduce the frequency of workplace accidents, promote operational efficiency, and support legal and regulatory compliance, making it a critical resource for anyone involved in construction work.

Study of the Hydrological and Erosion Characteristics of Typical Spoil Heaps in the Yangtze River Delta of China

Spoil heaps have become a major source of anthropogenic soil erosion, but the hydrological responses and erosion mechanisms of in situ slopes under rainstorms remain poorly understood. We performed simulated rainfall experiments at real estate (Site A), railway (Site B), and railway station (Site C) construction sites, as well as spoil sites (Site D) in China’s Yangtze River Delta. Rainfall parameters, surface runoff, interflow, vertical soil moisture profiles, and sediment yield were monitored: (1) Hydrological responses differed significantly across the sites due to soil structure complexity; stable erosion after the first rainfall event was not achieved at any site except Site C. Soil erosion was the strongest at Site C, followed by Sites D, B, and A. After the second rainfall event, erosion was stable, increasing, and decreasing at Sites A, B and C, and D, respectively. (2) Runoff and the soil loss rate were positively correlated (R2 > 0.7), and the slopes of the fitted regression lines were highest for Sites B and C, followed by Sites D and A. (3) Soil erodibility values based on field data were 0.0029, 0.1164, 0.1974, and 0.0989 t·hm2·h·hm−2·MJ−1·mm−1 for Sites A, B, C, and D, respectively. (4) The soil bulk density, gravel content, and silt content were key factors contributing to the severe erosion of field spoil heaps. Spoil heaps from different project types exhibited distinct hydrological and erosional behaviors, which necessitates targeted mitigation strategies to reduce severe erosion and landslide risks.

The effectiveness of safety signs on construction sites: A study of key influencing factors and action paths.

Safety signs are an important tool for safety management on the construction site. In order to cope with the current safety challenges faced by the construction industry, it is urgent for us to strengthen the in-depth research and wide application of its scientific and effective nature. Establish an index system of influencing factors for the effectiveness of construction site safety signs, identify key influencing factors, clarify the action path between factors, and put forward improvement measures, in order to significantly reduce the safety risk of construction site, ensure the safety of construction personnel and the smooth progress of operation. Social Network Analysis (SNA) was used to identify the key influencing factors and key action paths of the effectiveness of safety signs on construction sites. The influencing factor index system includes 4 first-level indicators and 35 second-level indicators, establishes a social network of influencing factors for the effectiveness of construction site safety signs. The results show that among the 15 key influencing factors, organizational factors account for a large proportion, revealing the important position of organizational factors in safety sign management. According to the analysis results of the action path, the degree of sign damage has a comprehensive impact on the effectiveness of safety signs, and the attitude of management and management measures have direct and indirect effects. Based on the research results, this paper analyzes and puts forward suggestions for improvement, in order to better improve the effectiveness of safety signs.