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Learning from experience approach has been widely adopted in the academic cluster of Built Environment including Civil Engineering field. To further strengthen this aspect, a study in exploring construction site visit as an aid to educational component in teaching and learning method is significant. This provides a platform which helps civil engineering students to enhance their safety knowledge and understanding by experiencing the real time situation at the construction site. The objectives of this study are to: (1) determine the effectiveness of site visit approach in teaching and learning of construction site safety; (2) measure the performance between students who acquire knowledge related to safety by experiencing one site visit compared to students with zero site visit; and (3) compare the achievement and understanding between male and female students if site visit is adopted in the teaching and learning process. A set of questions is designed as an instrument and participating students are required to answer the questions nonverbally to show their knowledge on the construction site safety. Based on the scoring marks, a statistical analysis has been conducted. The result indicates that students with one site visit have better achievement and understanding compared to students with zero site visit. In addition, the result also shows that with one site visit, female students have better performance in the score marks compared to male students.

Efficient planning for construction site layout is pivotal for the successful execution of a project, contributing to enhanced productivity and safety on the site. This involves identifying temporary structures or facilities required to support construction activities, choosing their size and arrangement, and strategic placing within the available space on the site. The problem of site layout planning is a challenging issue in combinatorial optimization, especially as it involves multiple objectives. Its complexity escalates with the increasing number of facilities and constraints. While existing research has proposed various analytical, heuristic, and meta-heuristic approaches to address this problem, many prior studies focused on a limited number of facilities, emphasizing the minimization of travelling distances while neglecting other pertinent cost-related and decision-making factors. This study aims to create practical and effective solutions for site layout by employing a realistic representation that takes into account not just travelling distance but also considers cost and safety relationships. A model for optimization with two objective functions has been developed to minimize travelling distance between facilities in order to minimize cost functions derived from various factors such as construction costs associated with different facility locations and transportation costs between locations, as well as to minimize risks based on the quantitative flow matrix and distance between facilities, as increasing in the frequency of interaction flow between facilities results in a higher probability of collision. In this research, a genetic algorithm (GA) is used as a heuristic optimization approach. A case study was applied to the model to highlight the benefits of the suggested approach, illustrating its effectiveness and comprehensive solutions for construction site layout planning.

The paper documents some of the results of the research program Smart Yard: Industry 4.0 Production Process aimed at reducing inefficiencies in construction site procedures by developing new management methods supported by digitisation processes that can memorise and monitor computational data and guide them towards specific purposes of production processes optimisation. In particular, the aim of the study is to propose evolved, highly efficient and innovative organisational and information management models that enable more reliable control of processes in the construction sector, mitigating unavoidable uncertainties while pursuing a more rational use of resources. The products developed to support the programme include two newly designed pieces of equipment with advanced functions, which can be configured as examples of site management systems such as a “smart factory” in the logic of Industry 4.0, illustrated below. One is a high-performance charging system for power banks to be used with portable power tools, while the other concerns a work standing desk that employs wireless communication protocols to dialogue with other devices at the construction site.

[Objective] Unsafe behaviors among personnel are significant factors contributing to safety accidents in rail transit engineering. To address the existing issues of video image monitoring systems, such as reliance on manual identification, delayed detection of safety problems, and inadequate real-time alarms, research on metro construction personnel safety management system is required. [Method] The construction scheme of the metro construction personnel safety management system is elaborated from the aspects of overall architecture, equipment installation for data collection modules, establishment of BIM (building information modeling), and software platform. The application of the above management system in specific engineering pilot projects is analyzed to verify the intelligent identification accuracy of the system. [Result & Conclusion] The application of the system on construction sites demonstrates that the system can intelligently identify unsafe behaviors of construction personnel, such as not wearing safety helmets, unauthorized entry into restricted areas, and sleeping or leaving posts, for which scenarios the accurately triggered alarms take up to 98%, significantly enhancing the precision and efficiency of identifying safety hazards. Relevant personnel can access surveillance videos in real-time through interactive terminals, receive alarm information promptly, swiftly locate the occurrence of hazards, and take necessary actions.

The swift integration of advanced technologies in the construction sector has significantly propelled the adoption of smart construction sites (SCSs). Quality management (QM), a critical endeavor within the construction domain, is central to the operational success of construction projects. The establishment of quality management in smart construction sites (SCS-QM) specifically seeks to delineate the principal factors influencing quality management in the context of SCS, with the objective of enhancing overall project quality. This study has identified 19 pivotal factors impacting SCS-QM by drawing upon the 4M1E quality management framework and an extensive review of the literature. Utilizing the hybrid DEMATEL-ISM-MICMAC analytical framework, the research evaluates these factors in terms of significance, hierarchical structure, and interdependencies, thereby formulating targeted strategies for the advancement of SCS-QM. Through a systematic evaluation by nine experts, this study categorizes the influencing factors into nine levels, three layers, and four areas, further classifying them into four distinct impact typologies. The results underscore that those technologies, such as automation and intelligence, along with regulatory frameworks, comprehensive quality management standards, transparency of critical technologies, training of construction personnel, and effective process management, constitute the foundational elements crucial for enhancing SCS-QM.

The number of accidents in the Korean construction industry has been increasing rapidly, reaching about 25,000 every year. Although strong and binding laws and systems have been implemented to reduce accidents in the construction industry, the frequency of accidents is still higher than that of other industries. While existing studies have provided models for predicting occupational accidents, there are limitations in predicting individual workers’ occupational accidents specific to site conditions and worker tasks. This study proposed a deep neural network model that can classify and predict core accident types for workers in construction sites, considering the characteristics of the site and workers based on 70,204 case data from the Korea Occupational Safety and Health Agency. The result of this study would contribute to improve safety in construction sites by being used for safety accident prevention training customized for workers at construction sites. Additionally, the model can be expanded to develop a real-time construction site occupational accident monitoring and early warning system by integrating ICT-based safety management technologies such as wearable devices and CCTV.

Background: Cervicodynia is one of the most common musculoskeletal symptoms reported by construction workers, particularly those involved in head-load carrying jobs. One of the reasons for this could be accelerated degenerative changes in the cervical spine as a result of continuous stress on it owing to head-load carrying. Apart from carrying a heavy load on the head, frequently have to work in an awkward posture involving “repeated movement of the back and limbs and work above the shoulder during loading on the head and unloading as well as stacking bricks.” In response to the continuous stresses on the neck muscles, Cervicodynia is a commonly occurring phenomenon among construction workers, particularly head-load carrying workers. Aims and Objectives: This cross-sectional study aims to determine the association between cervicodynia and head-load carrying in construction site workers in Lucknow. Materials and Methods: A total 68 workers were enrolled in the study and their details were recorded in the questionnaire Pro forma and they were asked to provide their job details. Those workers responding affirmatively to cervicodynia, the intensity, and its distribution along the brachial plexus and vertigo was recorded. Data were recorded on a separate case recording sheet, and statistical analysis was done using SPSS Version 21.0 statistical Analysis Software. The values were represented in number (%) and Mean±SD. The Chi-square test and Student’s “t” test was used. The level of significance was P

This research explores edge computing for construction site monitoring using computer vision (CV)-based worker detection methods. The feasibility of using edge computing is validated by testing worker detection models (yolov5 and yolov8) on local computers and three edge computing devices (Jetson Nano, Raspberry Pi 4B, and Jetson Xavier NX). The results show comparable mAP values for all devices, with the local computer processing frames six times faster than the Jetson Xavier NX. This study contributes by proposing an edge computing solution to address data security, installation complexity, and time delay issues in CV-based construction site monitoring. This approach also enhances data sustainability by mitigating potential risks associated with data loss, privacy breaches, and network connectivity issues. Additionally, it illustrates the practicality of employing edge computing devices for automated visual monitoring and provides valuable information for construction managers to select the appropriate device.

Environmental issues are often neglected in previous studies on site layout evaluation problems. To address this research gap, a comprehensive evaluation model is developed to evaluate construction site layout planning in this paper. The proposed evaluation model includes four aspects: (1) land use; (2) safety; (3) logistics; and (4) sustainability evaluations. To make the results of the evaluation more accurate, the evaluation model includes the subjective factors that need to be scored by the experts and the objective factors that could be gained from the inspections of the projects and the related materials. Then, the entropy weight method-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is chosen to solve the proposed multicriteria evaluation model. In this paper, the construction site layout planning for four different typical and practical cases, which include two commercial mixed buildings, a residential building, and a sewage treatment plant, is studied. The evaluation results proved that the proposed model is suitable to evaluate common types of construction, such as residential and commercial buildings but not specific functional types, such as the sewage treatment plant. [ABSTRACT FROM AUTHOR]

Construction activities have positive and negative impacts, one of the negative impacts is the generation of construction waste. As construction projects develop, waste generation also increases. As an effort to realize the concept of sustainable development in the health sector, the Directorate General of Health Services issued Guidelines for Environmentally Friendly Hospital (Green Hospital) in 2018. However, these guidelines do not contain guidelines for the implementation aspects of building construction. Therefore, the purpose of this study was to identify the factors, criteria, and indicators in the preparation of instruments for assessing aspects of the implementation of Green Hospital construction, and to analyze the results of the assessment. This research is qualitative using descriptive analysis method. The research results obtained 50 factors, 10 criteria and 3 indicators arranged in an assessment instrument called "The Construction Implementation on Eco Green Hospital Development". Analysis of the assessment results on the Phase I Integrated Emergency Room Building Construction Project of the K.R.M.T Wongsonegoro Semarang Hospital shows that the Green Construction Project Policy indicator with 4 factors out of 15 factors has been met, the Environmental Performance Management indicator with 4 factors meets out of 15 factors, and the Construction Waste Management indicator with 9 factors out of 20 factors has been met.

Construction sites are well known for the inherent risks that negatively impact the safety and well-being of workers. Identifying and minimising these hazards is critical for preventing accidents and creating a safe working environment. Traditional techniques of hazards identification in construction rely on visual assessments and professional expertise, which can be time-consuming and subjective. The goal of this research is to identify traits that indicate potential dangers in the construction industry by extracting meaningful information from accident narratives. This will be achieved through the application of a rule-based iteration approach, using the Natural Language Toolkit (NLTK) for keyword extraction and text tokenization. It is a branch of artificial intelligence and computational linguistics concerned with the interaction of computers and human language. The research methodology involves the utilization of NLTK and the application of a rule-based iteration approach to extract hazards from construction-related accident narratives. The proposed approach includes gathering accident narratives, pre-processing data, and textual analysis with NLP tool for information extraction and training the algorithm with identified attributes. The textual analysis eventually leads to the extraction of significant sources of dangers that cause accidents. The study contributes to the developing subject of construction safety management by utilizing the capabilities of NLP to enhance hazard detection, resulting in safer construction practices and lower occupational hazards. The findings emphasise the accuracy with which NLP approaches detect dangers, allowing construction professionals to proactively decrease risks and enhance overall safety on construction sites.

Introduction/purpose: The construction industry is one of the main producers of dust, greenhouse gases and air pollutants. Effective operation and management of construction site operations can significantly reduce projects' carbon footprints and other environmental impacts. Through the cooperation of a scientific and research institution and a construction company, the real-time monitoring of air quality at a construction site was implemented using IoT technologies. Methods: An IoT-based system framework that integrates a distributed sensor network to collect real-time data and demonstrates air quality at a construction site was implemented. Different types of sensors were used to collect data related to NO2, PM2.5, and PM10 particles, as well as meteorological parameters – wind speed and direction, humidity, pressure, and temperature. Results: The results of real-time measurements provide a picture of the state of air pollution at the construction site and the connection with construction activities that can be managed in order to reduce the concentration of polluting gases and suspended particles. Through on-site monitoring of a construction site in Belgrade City, this study found that the dust level due to construction activities is relatively high. Conclusion: It can be concluded that the construction activity had a significant impact on the air quality in the construction surrounding areas. Regarding the main factors affecting the building construction dust emission, the correlations show that building construction dust emission was not significantly correlated with meteorological factors.

[Objective] To address the issues of delayed, ineffective communication of information and declining instructions among various stakeholders during metro project construction, it is necessary to improve data integration, storage, and transmission in comprehensive monitoring system through digitalization and intelligent means. A DCCMS (digitalized construction-site comprehensive monitoring system) is established. [Method] Taking the construction of Guangzhou Rail Transit Line 3 East Extension project and the DCCMS for synchronous implementation of general contracting project as the research object, the construction content and overall architecture under metro project general contracting mode are outlined. The types and characteristics of users at construction management level, frontline management level, operation-maintenance management level are elaborated. Targeted designs for communication networks and functional scenes in the perception layer are conducted. The innovation in key technologies such as big data visualization dashboards, graphic engines, and emergency command system knowledge base management is analyzed. [Result & Conclusion] The construction system of DCCMS adopts the concept of ′hierarchical design and management′, which is deployed in Guangzhou Rail Transit Line 3 East Extension project and further improved through engineering practice. Through establishing DCCMS, the effective monitoring of personnel safety, construction safety, equipment safety, and construction process is realized. With research on key technologies, data integration, storage, and transmission in the construction-site comprehensive monitoring system are improved and emergency response is effectively supported. The establishment of a safety-controlled construction environment through digitalized construction-site perception system enables big data-driven decision-making and evaluation, providing reference and promotion for the informatization construction of comprehensive monitoring systems in metro construction projects.

Bengkulu City is situated within a subduction zone where the Indo-Australian and Eurasian tectonic plates converge, rendering the area highly susceptible to seismic activity. This study employs the microseismic method to assess seismic vulnerability and the subsurface rock structure at the Dendam Tak Sudah Lake Flyover Construction Site in Bengkulu City, which encompasses a swampy region. The microseismic method used was an inversion of the horizontal to vertical (H/V) spectral ratio (HV-Inv) for determining the dominant frequency (f0), amplification factor (A0), seismic sensitivity index (Kg), and shear wave velocity (Vs). The findings reveal that f0 in the study area range from 2.16 to 7.53 Hz, A0 vary from 0.40 to 3.79, and Kg values span from 0.03 to 6.04. The sedimentary layers exhibit an average thickness of 5-10 meters, with some locations showing significantly thicker sedimentary deposits. Notably, the highest seismic susceptibility is recorded at point T8. The Vs values range from 185.19 to 539.49 m/s, which are inversely proportional to the Kg values and indicate soil classifications varying from soft to medium. The overall seismic risk in the study area is moderate. These results offer key insights into geophysical and geological conditions in Bengkulu City, crucial for earthquake mitigation.

ABSTRACTAn inner-city building construction site for vacant land development or redevelopment of existing property is a site surrounded by buildings, occupants, and busy street(s); its space is the most important but limited resource that needs wisely planned and professionally used. The study aimed to assess the construction site layout planning (CSLP) practice and the classification of space requirement variables for inner-city building sites. Thus, the study adopted a triangulated study approach and the data was collected through a sequential mixed method that includes case studies: semi-structured interviews, document evaluation, and site visits observation followed by a two-round Delphi survey. The study has identified the space requirement variables, classification, and relationship for CSLP of inner-city sites; classified under three categories: Micro-space (four), Macro-space (18), and Paths (five); and have significant to strong associations with each other. The current practice shows the CSLP is overlooked or fragmented which causes a congested site condition. So, the provided insight on the strong relationship between space requirements will be a basis for shifting from the temporary-facility-centered planning methods towards an integrated CSLP approach to overcome contractors’ problems in the continued redevelopment demand of the inner city.

Context: Through a dual model, based on 2 years of education in upper secondary school followed by two and a half year of apprenticeship training, Norwegian plumbing education has become an integrated part of the Technical Vocational Education and Training (TVET). Competence and skills are described in national plumbing curriculum. However, there is little information on how learning and training, interaction and relations between craftsmen and apprentices take place and develops at the workplace. The objective of this article is to identify significant learning processes by studying apprentices in a training agency and in communities of plumbers at the building site. Approach: The study has an ethnographic approach, based on a combination of fieldwork and interviews with apprentices, plumbers and a vocational teacher. During one year of fieldwork I followed a group of plumbing apprentices in a training agency, and in their plumbing companies at different construction sites. As a former plumber and vocational teacher, I was able to participate as a plumber and researcher and thus I had a unique position to work along with the apprentices and plumbers. This enabled me to observe interaction, learning and training in their communities of practices as an "insider." Findings: The study showed that the process of learning practical skills, a professional language and a technical rationale was time consuming, challenging and sometimes tiering. In return the apprentices discovered proficiency, gained confidence and were considered as participants in the community of plumbers. A central finding is the great value of working in a community of plumbers at the building site, combined by studying sanitary and heating technology at the training agency. Among peers at the training agency, the apprentices were challenged to build and explore complex pipe laying, and to discuss technical regulations and rationale with each other and the vocational teacher. Conclusion: Craftsmen, like plumbers, consecutively handle a variety of technical work tasks and situations. "Inside" practice, the plumbers are close to materials and systems on construction site, where pipes, cableways and building structures looks different from the plan and the progress on paper. As experts, the plumbers often solve problems more functional and cost effective than suggested in the blueprint. The study shows how apprentices and plumbers develop skills, tacit knowledge and professionality through involvement with relevant "things" and "situations," and by sharing experiences and technical expertise in communities of peers and plumbers.

Collective self-construction sites are children of the time of crisis. They represent, when economic conditions turn into social urgency, an emergency solution, but at the same time experimental, connoting themselves as real architectural laboratories, with a strong democratic impulse. Starting from the description of some construction practices, implemented in São Paulo Metropolitan Region, the contribution proposes a critical reading of the theoretical speculations at the origin of these virtuous practices, recognized as a legacy of the design research of the 1960s-1970s of the Arquitetura Nova group. Indeed, the investigations of Sérgio Ferro, Rodrigo Lefèvre and Flávio Império inspired, and permeated, the organization of the site for houses and collective spaces areas, intended for the population without access to the housing market. Arquitetura Nova's aspirations and experiments confirm the affirmation of new management and realization processes. The constructive paradigm is completely overturned in order to break down the boundary between intellectual and manual labor. The project, designed a priori, loses its role to give way to individual contribution. The production of the group, partly or not at all known in Italy, focuses on a constant dialogue between all the actors involved in the construction: the challenge is to build some houses according to new relations and productive reasons.

The global push for sustainable energy has heightened the demand for green hydrogen, which is crucial for decarbonizing heavy industry. However, current electrolysis plant capacities are insufficient. This research addresses the challenge through optimizing large-scale electrolysis construction via standardization, modularization, process optimization, and automation. This paper introduces H2Giga, a project for mass-producing electrolyzers, and HyPLANT100, investigating large-scale electrolysis plant structure and construction processes. Modularizing electrolyzers enhances production efficiency and scalability. The integration of AutomationML facilitates seamless information exchange. A digital twin concept enables simulations, optimizations, and error identification before assembly. While construction site automation provides advantages, tasks like connection technologies and handling cables, tubes, and hoses require pre-assembly. This study identifies key tasks suitable for automation and estimating required components. The Enapter Multicore electrolyzer serves as a case study, showcasing robotic technology for tube fittings. In conclusion, this research underscores the significance of standardization, modularization, and automation in boosting the electrolysis production capacity for green hydrogen, contributing to ongoing efforts in decarbonizing the industrial sector and advancing the global energy transition.

This study examines how the linguistic landscape of a university in the midwestern United States has changed since the COVID-19 pandemic, and how that change has discursively constructed the identities of the university and its community. The focus lies in the newly displayed semiotics that provides information about preventing the virus from spreading. By analysing public signs such as flyers, posters, and banners whose contents have to do with COVID-19, this study found the following five ways in which the institution and community express their identities and voices. The university's identity has shifted to that of an agent that acts to encourage a united effort to protect itself and its community; a caring entity that cares about community members; a site for community members' voice expression; a space creator to expand interaction from physical to online discourses; and an information deliverer for international members of the community. This study calls for research that investigates the global pandemic's influence on the linguistic landscape.

In the rapidly advancing field of construction, digital site management and Building Information Modeling (BIM) are pivotal. This study explores the integration of drone imagery into the digital construction site management process, aiming to create BIM models with enhanced object recognition capabilities. Initially, the research sought to achieve photorealistic rendering of point cloud models (PCMs) using blur/sharpen filters and generative adversarial network (GAN) models. However, these techniques did not fully meet the desired outcomes for photorealistic rendering. The research then shifted to investigating additional methods, such as fine-tuning object recognition algorithms with real-world datasets, to improve object recognition accuracy. The study’s findings present a nuanced understanding of the limitations and potential pathways for achieving photorealistic rendering in PCM, underscoring the complexity of the task and laying the groundwork for future innovations in this area. Although the study faced challenges in attaining the original goal of photorealistic rendering for object detection, it contributes valuable insights that may inform future research and technological development in digital construction site management.

This study examines the impact of internships and the influence of construction site manager (CSM) as a role model on students' educational and career paths, employing Kolb's experiential learning theory as a theoretical framework. To address the research objectives, a questionnaire was conducted on 93 architecture students, focusing on four key research questions: (1) Does the internship experience affect students' perception of CSMs? (2) Does it influence their career goals related to CSM positions? (3) Does the internship experience alter the career aspirations of intern students regarding the career as a construction site manager? and (4) Do students perceive CSMs as role models after completing their internships? The findings show how internships significantly affect students, improving their comprehension of the CSM role, influencing their aspirations for their careers, and offering life-changing experiences. It is anticipated that these findings will significantly advance the field of architecture education and have applications in curriculum development, internship program design and execution, and career counseling services, especially in underdeveloped or developing countries.

The article analyses the options for the layout of the planned residential building on the site, assessing the acoustic level of the environment. The analysis is important because the area is next to a busy street and a railway line, and the building is intended to be used for the permanent residence of people with special needs. In the initial version of the model, the location of the building on the site is chosen 7 m from the street boundary. Due to the results of the model analysis – the sound pressure to the facades in 41% of the area exceeds the permissible values, to the roof – 30 %, to the ground – 24 % – the position of the building was adjusted, placing it another 9 m away from the street, and vegetation was inserted, a ditch filled with peat 0.9 m of depth in the ground is recommended at the south-western border of the plot.

The existing classical object detection algorithm cannot satisfactorily detect abnormal behavior of operators at smart construction sites for its low accuracy. To this end， a two-stage detection algorithm based on the FCOS （full convolutional single-stage target detection） is proposed to detect abnormal behavior at smart construction sites. The algorithm， mainly consisting of two cascaded networks， first identifies and locates operators and abnormal behavior markers by the FCOS， and then uses the MLP （multilayer perceptron） to detect and classify abnormal behavior. Finally， the 12，977 sample images collected from the relevant project sites are used as the data set to experimentally validate the detection algorithm. The results show that the algorithm performs well in the detection of all kinds of abnormal behavior， and it has a clear advantage in the deployment and application of practical projects due to its excellent real-time detection， less complex computation， and fewer model parameters.

Utilizing Unmanned Aerial Vehicles (UAV) and instance segmentation for construction site monitoring(such as construction machinery and operation surfaces) offers a significant leap in management efficiency over traditional manual supervision methods. However, in UAV-based remote sensing images, the subtle presence of construction machinery and the image features resemblances among various operational surfaces make it difficult to segment instances. To address these challenges, this study proposed a novel instance segmentation model based on the YOLOv8-seg model. Given the unique challenges, the proposed model makes three improvements to the original YOLOv8-seg model. First, the paper incorporates the FocalNext module, which extends the sense field of the convolutional kernel to capture contextual data and integrates multilevel features, enhancing the perception of local details. Second, the paper incorporates the Efficient Multiscale Attention (EMA) module, which refines image features by emphasizing spatial-channel interactions and adeptly contrasts patterns across scales to detect nuances overlooked by conventional models, aiding in distinguishing similar construction operation surfaces. Last, given the intricate nature of construction site images, this paper incorporates the Context Aggregation module, which enhances pixel analysis by intelligently modulating feature weights to highlight essential global contexts. The ablation experiment demonstrates that the enhancements perform well on the YOLOv8-seg two variants model. Comparative experimental results show that the improved model significantly outperforms existing instance segmentation models regarding model performance, complexity, and inference speed. Overall, the improved YOLOv8-seg model balances model performance and computational complexity to meet the needs of edge device deployment in field monitoring.