Large-scale Construction Sites Research Articles

He taught to build not only cheaply, capital, but also beautifully

Introduction. In construction science, the name of Professor Boris Grigoryevich Skramtaev is widely known to the engineering community of the country and far beyond its borders. He actively participated in the formation and development of the Russian scientific school of concrete and reinforced concrete. The construction industry actively implemented the innovation of researcher Skramtaev and his colleagues, using precast concrete proposed by scientists on large-scale construction sites, introducing new types of cements developed by them for the construction of residential and industrial facilities.
 
 In Boris Grigoryevich's informative biography, in addition to research on hydraulic engineering, coarse-porous and high-strength concretes, participation in the development of inorganic binders is vividly reflected. He devoted a lot of time and effort to leading research teams, the results of which are impressive by today's standards. Thus, during the difficult for the country war period, scientists under the leadership of Professor Boris Grigoryevich Skramtaev contributed to the full and timely provision of domestic industry with building materials, primarily high-quality concrete and reinforced concrete, even in extremely difficult periods for the construction industry during the Great Patriotic War and the post-war restoration of the national economy.
 
 The aim of this article is to conduct an author's analysis of the fruitful scientific activity of Professor B.G. Skramtaev, to confirm his talent as a leader of research teams. To trace the biography of the outstanding Russian scientist Boris Grigoryevich Skramtaev, the founder of the Russian school of reinforced concrete, who made a great contribution to the creation of modern building materials, various types of concrete, which were actively used in the construction industry.
 
 Results. The article examines and analyzes the newly discovered biographical data about Professor B.G. Skramtaev, analyzes the influence of the environment in which the scientist's worldview was formed, the motives of his active and fruitful activity in the field of concrete science. For the first time, the responses of representatives of the scientific community and heads of interested ministries of the country about B.G. Skramtaev's popular textbook "Building Materials", which has undergone five reprints, has become a reference book for several generations of students and engineers.
 
 Conclusions. The source base of the research has been expanded, archival documents and materials that were previously unknown or little known to the scientific community have been introduced into scientific circulation. Several materials have been found confirming the heuristic outstanding abilities of researcher B.G. Skramtaev in the field of concrete science.
 
 The role and place of scientific activity of Professor B.G. Skramtaev as a theorist on the creation of new promising concretes and mixtures for the construction industry are determined.
 
 In the article, the scientist B.G. Skramtaev is shown as a versatile and in-depth researcher, the author of numerous textbooks and monographs, relevant articles on the topics he studies. For example, his 1952 textbook "Building Materials" reflects the priority of Russian science and technology more fully than previous editions. It presents the achievements of Soviet construction materials technology and changes in outdated terminology on building materials. The fifth edition provides new data on materials and reinforced concrete parts for prefabricated construction, takes into account the basic principles of the draft Schedule, replenishes the section "Achievements of domestic science and practice", reflects the works successfully implemented in practice in 1950–1952, including personal works of the authors, especially in the field of new cements, concrete technology and building solutions.
 
 The conclusions obtained as a result of the analysis of the material contribute to the study of the originality of the versatile personality of B.G. Skramtaev, his phenomenon in scientific thought in the field of concrete science and building materials in the period of the 1930s–1960s.

Using Spatial Scan Statistics and Geographic Information Systems to Detect Monthly Human Mobility Clusters and Analyze Cluster Area Characteristics.

This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS). The research area covers approximately 10.3 km2, with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas. Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites. Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.

3D reconstruction of large-scale scaffolds with synthetic data generation and an upsampling adversarial network

Falls from scaffolds cause the majority of accidents and fatalities at construction sites. A deep learning-based 3D reconstruction technology could provide a solution to prevent such fatalities through automated scaffold monitoring. However, when the technology was used at a large-scale construction site, there were limitations, such as the scarcity of point cloud data and the non-uniformity of points. To address this issue, this paper presents a large-scale scaffold reconstruction method using synthetic scaffold datasets and an upsampling adversarial network. The method consists of four steps: 1) data acquisition of scaffold point cloud through a mobile laser scanning (MLS) system, 2) 3D semantic segmentation using synthetic datasets, 3) upsampling of the segmented scaffold points, and 4) automatic generation of a 3D CAD model. The performance of the segmentation model trained with synthetic datasets achieved an 80.83% F1 score, which improved to 94.93% after upsampling.

Identifying Critical Factors and Trends Leading to Fatal Accidents in Small-Scale Construction Sites in Korea

Small-scale construction sites in South Korea account for about 91.5% of all construction workplaces and contribute to 72.3% of the total accidents and fatalities. Safety measures at these sites are often underestimated, and proper safety education is lacking. In particular, the fatality rate is about 4.43 times higher compared to medium-/large-scale construction sites. In this study, a systematic analysis was conducted to examine the causes and trends of industrial accidents in small-scale construction sites to address these issues. This study analyzed industrial accidents in small-scale construction sites using statistical analysis, LDA topic modeling, and network analysis based on data from the Korea Occupational Safety and Health Agency (KOSHA) from 2018 to 2022. The analysis revealed that the most critical cause of accidents in small-scale construction sites is ‘Scaffolding and working platforms’, with accidents primarily involving ‘Fall’. Furthermore, various risk factors and accident trends were identified in apartment construction, new building projects, and mobile scaffolding usage. This study systematically analyzed the causes and trends of industrial accidents at small-scale construction sites, providing important evidence to enhance safety management and preventive measures. The results are expected to play a crucial role in establishing a safety culture at construction sites and ensuring the wellbeing of construction workers.

Small and overlapping worker detection at construction sites

Although there has been study on worker detection using computer vision (CV) for the safety of construction sites, it is still challenging to identify employees who are obstructed or have poor vision. To solve these problems, we propose a method of small and overlapping target (worker) detection at a complex construction site named SOC-YOLO. The method is based on YOLOv5 and utilizes distance intersection over union (DIoU) non-maximum suppression (NMS), incorporating weighted triplet attention, expansion feature-level, and Soft-pool. Workers can be captured with overlap, particularly in large-scale construction sites, using the DIoU-based loss function, and NMS contributed to accuracy improvement. Next, we propose a weighted-triplet attention mechanism that can extract feature information from space more effectively and channel attention when learning object detection networks, using a simple average approach based on the same weight between the existing triplet attention. Next, we propose a model that adds additional predictive heads and residual connections to address the poor detection accuracy of workers photographed over long distances. A low-level feature map containing more information regarding small targets is used by extending the feature level. Finally, Softpool-spatial pyramid pooling fast (Softpool-SPPF) is proposed to solve the problem of inconsistent input image sizes. Softpool-SPPF performs an spatial pyramid pooling (SPP) function while preserving more functional information for accurate small target detection. Experiments were conducted using published worker detection datasets and handmade datasets, and the results showed increase from 81.26% to 84.63% average precision (AP) for small objects, from 67.52% to 73.88% mAP for minute objects, from 74.56% to77.57% for overlapping objects. The proposed method is expected to be useful for safety monitoring by applying it to the construction site worker tracking model.

Signal Processing and Alert Logic Evaluation for IoT–Based Work Zone Proximity Safety System

Construction projects are dynamic by nature because of continuously moving resources such as heavy equipment and workers. This nature necessarily results in proximity hazards, especially on a large-scale construction site. Especially, struck-by accidents still account for about 38% of the total injuries in the US construction industry. Although there have been several efforts to mitigate the hazards, the statistics show that the hazards still persist. To provide a practical solution to this problem, this study proposes an Internet of Things (IoT)-based proximity warning system that provides an alert to workers whenever they are close to heavy equipment. The system includes equipment protection units (EPUs), personal protection units (PPUs), and Bluetooth low energy (BLE) beacons. A framework of signal processing and alert logic was developed to estimate the distance between PPUs and EPUs and to activate alerting modules timely. By calculating the distance based on the signal strength using a particle filtering method, EPUs and PPUs provide auditory and vibration alerts to equipment operators and workers when they are in an alert range. Also, practical alert logic was developed based on the site worker’s feedback. This study validates the system performance with different signal processing methods and alert logic through five real-world field tests. The system achieved a precision, recall, and F1-score of 89.21%, 97.45%, and 0.931 from the field tests, respectively. Also, positive feedback was obtained from the participating workers. The proposed IoT-based proximity warning system has a high potential for a practical solution to proximity hazards in actual construction sites.

Progress Monitoring at Construction Sites Using UAV Technology

One of the monitoring jobs in construction projects is conducting monthly surveys for monitoring the work done on the site. However, monitoring progress common for large-scale construction sites is arduous and time consuming. The manual method for monitoring a project is by taking pictures at the construction site using ordinary digital cameras that lacking in ability to capture the overall condition, especially for building structures that are too high or construction sites that are enormous. This study used UAV technology to help control and monitor the progress of monthly construction work at a construction site through visual image recording of the entire construction site. The study was conducted by recording images periodically for three months, which were then processed through Pix4Dmapper peripherals. This effort used photogrammetric UAV technique to achieve the main purpose. The study area was monitored to distinguish active and inactive areas from the first month to the third month in which orthomosaic images were taken to be analysed through Global Mapper software. The study proves the feasibility of using UAV as an alternative in visual monitoring and facilitating project management, as it is able to produce a more detailed view of the entire construction site area for each progress.

Reinforcement of Concrete Shoring Systems by Prestressing

Reinforced concrete piles are useful structural elements to support deep excavations. A pile wall is usually supported by one or several rows of anchors, depending on the depth of the excavation and the nature of the soil retained. The purpose of this work is to investigate the efficacy of posttensioned piles in retaining a 10.0 m deep excavation without using tieback anchors. In addition to the conventional passive steel reinforcement, the piles in this system include steel strands placed eccentrically in their sections, and they are referred to as posttensioned piles. The performance of posttensioned piles is investigated using the finite element modeling software PLAXIS 2D. The results are experimentally validated on a large‐scale construction site. The horizontal displacement of posttensioned piles in a 10 m deep excavation was found to be within allowable limits with a 7.36% difference in the horizontal displacement of pile top at the final excavation level in PLAXIS 2D. In terms of cost, PTP is executed at 35% cost less than the conventional reinforced method.

Waste generation, composition, and handling in building-related construction and demolition in Hanoi, Vietnam

This study conducted a survey at 15 building construction and demolition sites in Hanoi, Vietnam in order to identify waste generation rates (WGR), composition, and current handling practices of construction and demolition waste (CDW). Waste quantification based on CDW layout, image analysis to identify CDW components, and face-to-face interviews with construction and demolition contractors to reveal CDW flows were performed. WGRs of 79.3 kg/m2 and 1,030 kg/m2 were determined in small- and large-scale construction sites, respectively, whilst WGRs at small and large demolition sites were 610 kg/m2 and 318 kg/m2. The composition analysis identified soil, concrete, and brick as the major CDW components, consistent with building structures in Vietnam. The interviews discovered that merely 10% of total CDW flows was from recycled and reused CDW. Reuse and recycling rates were most significant for metal and were lower (in descending order) for brick, concrete, and soil. These findings raise a need for aggressive and integrated strategies to promote more sustainable CDW management in the country, including the development of recycled CDW product standards, policies that facilitate recycling, and more importantly, a sustainable business model for CDW recycling, for which future evaluations of economic feasibility are of great importance.

Automatic pipe and elbow recognition from three-dimensional point cloud model of industrial plant piping system using convolutional neural network-based primitive classification

With the recent development of laser scanning technology, the variety of applications of laser scanners has increased. One typical application is object recognition from laser-scanned point cloud models. On large-scale construction sites such as refineries and industrial plants, object recognition from point cloud models has been widely employed for construction progress monitoring, assembly inspections, and maintenance purposes. Pipelines are among the main objects of interest with regard to object recognition on such sites. There has been extensive research on recognizing pipes in pipelines; however, research on recognizing pipe-connecting elbows is still lacking. Most representative elbow recognition methods are centerline-based and connectivity-based methods. These methods do not use laser-scanned points directly; instead, they employ feature values that are calculated from laser-scanned points. However, these feature values are easily affected by noise and occlusion; therefore, the elbow recognition results could be inaccurate owing to noisy and occluded point cloud models. In this paper, we propose an automatic pipe and elbow recognition method robust against noise and occlusion in which pipes and elbows are recognized directly from laser-scanned points. This method starts with pipeline extraction, followed by elbow classification based on curvature information. Falsely classified points are filtered using convolutional neural network-based primitive classification. After elbow recognition is completed, pipe classification and recognition are performed. Experimental results obtained from three different point cloud models demonstrated that the proposed method recognizes pipes and elbows with high accuracy from noisy and occluded point cloud models.

Observer Design for non-stationary oscillating Disturbances in Mixing Processes

Information on the progression of the mixing process and quality are essential for mass concrete production. The power profile during the course of mixing can be used to evaluate the mixing process and its quality. Thus, a method for monitoring the average power transferred into the mixing material is presented, which enables an online mixture control based on the mechanical properties of the materials. An extensive experimental program on a 60 l laboratory twin-shaft mixer and a large-scale construction site mixer was performed to characterize mixer specific effects. For example, losses and speed dependent mixer shaft effects are examined. The portability and scalability of the results of the laboratory mixer is demonstrated by a comparison with a large construction site mixer. Based on the experimental insights, a signal model is derived which accounts for the characteristic disturbances in the power profile. Secondly, an extended Kalman filter is designed to estimate the disturbances online to separate these from the mean power signal, which can be used for evaluation of the concrete mixing progress. Finally, experimental results for different operating conditions and a comparison to classical signal filters such as a moving average filter and a low pass filter are presented, which show the increased performance of the presented approach.

Suppression of Soil Dust Emissions from Large-Scale Construction Sites Using Starch and Polyvinyl Alcohol

Soil dust emitted from large-scale construction sites in urban areas impacts air quality and creates a severe health threat to residents. Water spraying is commonly practiced to lower dust emission in construction sites, but its long-term effectiveness is questionable. In this study the utility of starch, polyvinyl alcohol (PVA), and a blend of starch and PVA in various proportions was investigated for the suppression of soil dust emissions at construction sites in Seoul. The efficiency of each dust suppressant was tested with test soil samples in a laboratory-scale wind tunnel box under different concentrations of suppressants and soil textures. Starch and PVA showed superior ability to suppress soil dust emissions compared to moistening bare soil, resulting in PM10 lower than the daily limit values of 30 μg/m3. PVA showed higher soil dust suppression capability for all conditions over starch. Test soils sprayed with dust suppressants significantly improved aggregate stability compared to untreated soils.

Efficacy of Starch and PVA (polyvinyl alcohol) for the Suppression of Soil Dust Emissions from Large-scale Construction Sites in Urban Areas

Efficacy of Starch and PVA (polyvinyl alcohol) for the Suppression of Soil Dust Emissions from Large-scale Construction Sites in Urban Areas

Occupational Health Problems of Construction Workers in Valley of Kashmir

Occupational risks have been classed as the tenth leading cause of morbidity and mortality and occupational skin diseases are important problem in the construction industries. Aim: The aim of this study is to understand the occupational health problems of construction workers engaged at large construction site in Kashmir. Materials and methods: The cross-sectional study was conducted at a large scale construction site of a cement factory on outskirts of Srinagar.All workers (N=200) were enrolled as study subjects. Data was collected by the team consisting of investigators, doctors and social worker using a pre tested validated structured preformed. Result: Among 200 workers only 28.0% used protective measure and 70% had suffered with at least one form of disease. Among them different diseases were found in following percentages i.e. respiratory problems (45.7%), musculo-skelekal problems (32.8%), Dermatitis (78.6%), burn/scald (6.6%) and accidental injury (28.6%). Conclusion: Measures are needed to improve the work environment of construction workers by ensuring availability of protective gears, sanitation facilities at the sites along with an accessible, accountable occupational health services

EVALUATION OF SAFETY AND HEALTH CONDITIONS IN LARGE SCALE CONSTRUCTION PROJECTS

In the past decade, a lot of studies and researches have been done on the problems concerning the management of safety and health in construction sites. But, there are very few studies discussing the issue in project environment from viewpoint of workers. Especially, safety and health management, which has been usually subject of the empiricism, is not considered deeply on the large scale construction sites in Iran. However, it is necessary to understand the quantitative mechanism of safety and health in project site to proceed the necessary management technique properly, and set up the suitable strategies by trade-off of risk and cost to reduce the safety and health risk during construction of the project. In this paper, employees of several large scale construction projects in metropolitan areas of Tabriz were randomly selected and were asked to respond the NOSACQ questionnaire. The results showed that safety and health management in large scale construction sites are relatively acceptable. Besides, the perception of safety and health was found to have no special association with age, work experience, education, but instead the creation of a safety climate depends on the approach of people higher in the organizational hierarchy of the project. Keywords: construction, health, safety, sustainability.

Improvement of inspection system for reduction of small-scale construction site accident in Korea.

This study analyzed the trend of construction accidents focused on the small-scale construction sites and the correlation between the ratios of official inspection by government and total fatality injuries per 10,000 people (TFR) by construction size in Korea. The method is to analyze statistical data for the construction accidents and official safety inspection system. In construction, accidents rate and TFR are recently increasing unlike other industries. In addition, the smaller the scale of construction sites, the higher the TFR, and vice versa. The smaller the scale of construction sites, the greater the degree of difference in the TFR for each year, and vice versa. In small-scale construction sites, which have amounted less than $273,000 (£231,000), approximately 45.7% of deaths occurred on sites between $36,400 (£30,800) and $182,000 (£154,000). The ratio of inspection, which represents the ratio of official inspection proportion to site share, is in inverse proportion to the TFR by construction size. As the ratio approached zero, TFR became higher, and the higher the ratio, the lower TFR since the official inspection was mainly carried out in medium and large-scale construction sites. To get an effect of official inspection, the focus of official inspection should move onto the small-scale construction site.

UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching

Abstract As construction sites increase in size, it becomes more difficult for a manager to understand the status of the site on time. However, with the development of unmanned aerial vehicles (UAVs), it is possible to collect a large amount of visual data of the construction site in a short time. Using this data, a large-scale construction site can be monitored in a timely and frequent manner with computer vision technologies. This paper proposes a method to generate a panorama of a construction site by using an image stitching technique with a focus on preprocessing. To create high-quality panoramas, blurred frames of videos are filtered out, key frames are selected, and camera lens distortion is corrected. The proposed method produced a high-quality panorama of a construction site, which was evaluated by comparing it with an aerial photograph and the panorama produced by the existing image stitching technique. The proposed method is expected to help managers to easily identify various construction site conditions with the help of high-quality image data.

단지형 다세대주택신축사업의 안전경영모델 표준화 및 적용사례 연구

최근 산업재해의 전반적인 감소에도 불구하고, 건설업은 증가추세이다. 사고의 대부분은 안전관리자가 선임되어있는 규모가 큰 공사현장보다 다세대주택, 리모델링, 근생시설 등 규모가 작은 공사현장에서 많이 발생한다. 실제 사업장 규모별로 보면 전체 재해의 81.6%, 대부분이 120억원미만 50인미만 소규모 사업장에서 발생됐다. 본 연구에서는 단지형 다세대주택신축사업을 추진하는 소규모 주택업체의 안전관리 운영실태를 조사하고 분석하여 효시적으로 안전경영모델을 표준화시켜 적용했으며, 소규모 주택업체가 표준화된 안전경영모델을 쉽게 이해하고 실천하여 무재해를 달성하는데 목적이 있다. Notwithstanding industrial accident decrease generally, Construction industry is on the rise recently. Most accident occurs in the small scale like as multiflex housing, remodeling, neighbourhood facility more than safety supervisor appointed large scale construction site. By the size of business place, 81.6% of total accident occurs mostly in small scale site that is bellow 120 billion won and 50 manpower. This paper aimed that small scale housing company achieve a zero hazard practising and understanding the safety adminstration standar d model easily and it applied firstly after investigating the operation condition of construction site safety control in small scale company propelling the complex style multiflex housing business.

A process to graphically demonstrate distance errors associated with local ground-based coordinate systems

This paper describes an empirical process for graphically illustrating, using any particular distance accuracy specification, boundaries where significant errors could be introduced in derived ground distances as a result of using local ground-based coordinate systems. The process involves selecting three-dimensional points across the project site, replacing their heights with combined scale factor corrections, and creating contours to represent potential distance errors. The resultant contour map shows potential distance errors in parts per million for comparison with accuracy specifications. The effectiveness will depend on the spacing of the selected points and the topography of the project site, but on most projects today closely spaced three dimensional points are readily available. The benefits were demonstrated on an example site, with the conclusion that the process is robust and effective, and will have applications on many large scale construction sites, residential estates, and other surveys where local ground-based coordinate systems may be used.

Using Swap‐Based Search Trees to obtain Solutions for Resource Constrained Project Scheduling Problems

AbstractThe high level of complexity in modern construction projects causes the problem of scheduling the involved tasks under precedence and resource constraints to be a great challenge. Additionally applications on real construction sites require non linear and non continuous constraints and objective functions. In order to optimize large scale construction sites in practice, this paper proposes a new optimization approach based on creating search trees from swapping and delaying tasks. The convergence of the proposed algorithm is analyzed and its complexity is compared to other available algorithms. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)