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Construction in cities and agglomerations is one of the main sources of air pollution in most countries in the world. Fine dust particles, PM0.5–PM10, which form as a result of construction processes, are among the most dangerous pollutants. With the increase in the volume of point-pattern housing development in cities, the task of maintaining clean air and environmental conditions becomes important. This requires research, the monitoring of dust emissions throughout the entire construction period and the development of design solutions based on the results obtained. The study examines the determination of the dispersed composition of dust generated on a construction site. A graphical representation of the dispersed composition is given by constructing integral curves on a logarithmic grid and approximating them using two-link and three-link splines. The gravimetric measurement method was used to analyze the concentration of dust in the air released during construction work near residential areas. Dust analysis at the construction site revealed significant differences in particle size that cannot be explained by statistical errors alone. The reasons for this are both working conditions and climatic factors, including humidity and wind intensity. In this regard, it is preferable to use models that take into account random processes instead of traditional deterministic methods to study the dust that shapes during construction.

With the growing trend of urbanisation and the growing number of people migrating to cities, the demand for the development and construction of new buildings and infrastructure has risen, meaning that the construction industry must adapt to these trends. Growing demands with shorter deadlines for an industry already known for its high costs and late delivery means that productivity must be increased without increasing costs. The solution for this might lie in the application of the Lean philosophy to the construction industry. This paper analyses the application of the Lean philosophy in order to increase the productivity of construction work for an airport project. This paper highlights the potential for enhancing productivity in construction workplaces by concurrently fostering continuous improvement and sustainability through the implementation of the A3 methodology and Lean principles, resulting in waste reduction and increased value.

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.

Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; however, they require real-time recognition automation solutions based on computer vision. Although various deep-learning-based models have recently provided new ideas for identifying helmets in traffic monitoring, few solutions suitable for industry applications have been discussed due to the complex scenarios of construction sites. In this paper, a fast and robust network based on a mutilscale Swin Transformer is proposed for safety helmet detection (FRSHNet) at construction sites, which contains the following contributions. Firstly, MAE-NAS with the variant of MobileNetV3’s MobBlock as a basic block is applied to implement feature extraction. Simultaneously, a multiscale Swin Transformer module is utilized to obtain the spatial and contexture relationships in the multiscale features. Subsequently, in order to meet the scheme requirements of real-time helmet detection, efficient RepGFPN are adopted to integrate refined multiscale features to form a pyramid structure. Extensive experiments were conducted on the publicly available Pictor-v3 and SHWD datasets. The experimental results show that FRSHNet consistently provided a favorable performance, outperforming the existing state-of-the-art models.

Environmental problems including the depletion of natural resources and energy have drawn a lot of attention from all sectors of society in the context of high-quality global development, and solid waste generated by the construction industry accounts for 36% of the total amount of municipal waste. The generation of large amounts of construction waste not only causes a waste of resources, but also causes great damage to the environment. Reducing the quantity of solid waste produced during a building’s new construction period can be greatly aided by construction site solid waste statistics and forecasts. Based on the statistical data of 61 public construction projects in Hainan Province, China, this study uses the Random Forest algorithm to rank the importance of possible factors affecting the amount of solid waste generated, and linearly fits the data to achieve the prediction of solid waste at construction sites. The findings indicate that building area, building height, concrete usage, steel usage and assembly rate are the main factors affecting solid waste in construction sites. In office buildings and exhibition buildings, an increase in ground area, building height, concrete usage, and steel usage increases the generation of each type of solid waste (inorganic non-metallic solid waste, metallic solid waste), with the exception of an increase in concrete usage, which results in a decrease in the generation of metallic solid waste. Furthermore, a higher assembly rate can substantially lower the production of all waste types. These results offer a theoretical foundation for the implementation of assembly construction to support the high-quality development of the construction industry, as well as partial design inspiration for the architectural design stage.

The building industry faces a number of prominent challenges in the coming period. In this article, we focus on productivity in construction, which lags behind other industries despite technological developments. There is an urgent need for more efficient production methods. In other words, the potential for increasing productivity in construction is enormous. As in other industries, the key to this lies in process orientation, process standardization, and digitization. Lean construction approaches offer innovative solutions here by aiming to maximize customer value while minimizing waste, applying the principles of lean production to construction processes. However, building products are distinct in nature. Efforts to standardize them have achieved partial success, but only within specific product categories and for certain customer needs. Most construction activities remain highly unique. An alternative solution lies in standardizing work processes and not the final product. By adopting this method, one can considerably decrease individuality in production without compromising the essential uniqueness of the building product. Consequently, it is crucial to gain a deeper understanding of the standardization of production processes and the dynamics within the construction sector. This article introduces a modular construction toolkit designed to standardize production processes at construction sites. This toolkit consists of a series of consistent process steps, each linked to a standard time metric. Using this classification, a production model is constructed from a select number of recurring processes, leading to an ontological representation of production. This modular approach allows diverse production processes to be compared based on productivity, as they are composed of consistent and comparable sub-processes. Such a comparison is crucial for continuous production optimization. This method also enables the pinpointing of the most wasteful processes across various construction sites. While the primary data generation use case is centred on special civil engineering (special foundation engineering), the core concepts can be applied to general building construction.

High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

To ensure the safety of construction site personnel and to improve the efficiency of emergency safety evacuation of site personnel, this study analyzes the risk reasons for fire accidents and the characteristics of combustion fires on construction sites. Based on a refined BIM model, a numerical simulation of the fire situation is performed using PyroSim (2019 version) software on a construction site. In the Pyrosim fire simulation model, fire scenarios with distinct construction stages and fire source locations are set up to simulate, compare, and analyze the varying pattern of each fire product in various fire scenarios. Using this information with the Pathfinder (2019 version) simulation model, a coupled simulation test of fire evacuation is conducted to assess the safety of evacuating individuals in each fire scenario. The results show that flammable materials in open spaces are more risky to burn than in confined spaces. After optimizing the utilization of safety exits and the density of people in the second simulation, it was found that the required safety evacuation time was reduced to 267 s, which is lower than the available safety evacuation time of 318.5 s for each scenario. All fire scenarios meet the safe evacuation criteria. The study results can provide a theoretical basis for developing fire response strategies for construction units and contribute to site safety management.

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers’ unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

Beetles are one of the most diverse and often highly specialized groups among saproxylic organisms and play a key role in forest dynamics. To develop conservation plans in forests threatened by human activities, such as construction sites, it is crucial to identify key parameters characterizing forest structure in turn influencing saproxylic beetle diversity and abundance. Here, we investigate the difference in forest structure parameters and their cascading effect on saproxylic beetle communities between a forest site affected by the construction site expansion of the Turin–Lyon High-Speed Railway Line and a nearby second forest site. Our study showed differences in forest structure parameters between the two sites, in particular in the overall volume and diameter of coarse woody debris and in standing dead tree abundance. Even saproxylic beetle community structure was different between the two sites and this difference was linked to the different abundance and species richness of standing dead trees. Our findings provide information for the development of a local conservation plan for the saproxylic beetle community within forest habitats. Increasing the volume of deadwood and enriching tree diversity can locally sustain abundant and diverse communities and, eventually, support those species that are threatened by the building site expansion.

In historic centers, construction works consist of complex activities that must balance the operative requirements and lower the impacts on a delicate and sensible environment. In this urban system, especially regarding relevant reconstruction processes such as post-natural disaster scenarios, construction operations are performed through the traditional construction processes, using fuel-based generators and vehicles with limited efficiency and with relevant impacts in terms of the consumed energy, noise and vibrations. In the global transition of the construction sectors towards a zero-emission and fuel-free future, construction sites in historic centers represent a particular opportunity where the application of fuel-free strategies is particularly feasible and can provide additional value in terms of the environmental impact, productivity and health and safety. This work addresses the need for a framework to provide the basis for the application of fuel-free principles in construction within historic city centers dealing with two major concepts: the adaptive construction site as a way to reduce the energy demand and the potential adoption of fuel-free machines. The former is derived from the analysis of a real project in the historic city of L’Aquila, while the latter is defined through the identification and categorization of the applicable electric machines, equipment and vehicles and the discussion of the limits, opportunities and added value of the fuel-free strategies.

Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site.

Chalk lines are used to print layouts in construction sites to indicate the location of attaching or cutting objects; printing depends on the skills of workers and is suitable for small-scale work. Moreover, this type of work requires a precise measurement process, which is time-consuming, to avoid errors. Thus, discrepancies between blueprints and construction site layouts can occur if construction plans and management are not uniformly aligned. To improve the traditional floor-layout-printing technique on construction sites, this study introduces a manually operated mecanum-wheeled mobile robot in the preliminary stage, i.e., before the development of a full-fledged automated system. This manually operated robot helps determine the technologies required for robotic automation. In the development process, layout-printing technology is classified into a marking toolkit, control system that can be manually operated, and mobile driver. To improve layout-printing quality, this study adopted a mecanum-wheeled design to improve mobility. In this study, applied tests are required to consider the site environment for automatically marking floor layout prints. To determine the applicability of the developed technology, this study conducted a field applicability experiment with a pen-type marking module and laser-type toolkit. The experiment confirmed that layout printing based on environmental changes on the construction site can be manually performed using the mobile robot system. To automatically mark floor-layout-printing work, it is necessary to consider the floor characteristics on the construction site. In addition, this experiment shows that the newly applied laser toolkit technology can be applied to layout printing within 12 mm from the floor. To apply this mechanism to a mobile robot that can automate layout printing, it is necessary to technically enhance the optimization of marking quality, e.g., floor separation distance and marking thickness.

3D point cloud data (PCD) can accurately and efficiently capture the 3D geometric information of a target and exhibits significant potential for construction applications. Although one of the most common approaches for generating PCD is the use of unmanned aerial vehicles (UAV), UAV photogrammetry-based point clouds are erroneous. This study proposes a novel framework for automatically improving the coordinate accuracy of PCD. Image-based deep learning and PCD analysis methods are integrated into a framework that includes the following four phases: GCP (Ground Control Point) detection, GCP global coordinate extraction, transformation matrix estimation, and fine alignment. Two different experiments, as follows, were performed in the case study to validate the proposed framework: (1) experiments on the fine alignment performance of the developed framework, and (2) performance and run time comparison between the fine alignment framework and common registration algorithms such as ICP (Iterative Closest Points). The framework achieved millimeter-level accuracy for each axis. The run time was less than 30 s, which indicated the feasibility of the proposed framework.

The problem of the containment of fine dust (especially PM 2.5 and PM 10) emitted into the atmosphere is particularly acute, especially in industrialized countries. However, there are particular areas where it is still not adequately considered. One of these is the construction site sector. The aim of this work is to design a flexible, economical, and easy-to-use system, which allows for the detection of the emissions produced in critical circumstances such as the demolition of a building. To this end, a data logger and five customized nodes were designed through a five-step method. The data logger is able to transmit data to a PC, making them available in real time. The study was conducted on a reconstruction site in L’Aquila, Italy, a city severely affected by the earthquake in 2009, for two working days and a public holiday. Even if not presenting substantial critical issues in relation to the latter, the experimental results show that the emissions of PM 2.5 and PM 10 detected during the demolition activity far exceed, in some moments, the threshold values. In fact, peaks as high as about 123 μg/m3 for PM 2.5 and over 1000 μg/m3 for PM 10 have been detected.

Fire at a construction site usually results in serious accidents. Therefore, fire management at the construction site is critical to decreasing possible accidents. However, conventional fire safety management can be problematic in many aspects, such as visualization, multi-stage alarm systems, and dynamic escape route optimization. To solve these issues, this paper develops a platform for a BIM-based fire safety management system that considers the construction site. The developed platform contains four subsystems: a remote monitoring subsystem, a fire visualization subsystem, a multi-stage fire alarm subsystem, and an escape route optimization subsystem. It detects the fire hazard in the early stage of the fire by the remote monitoring subsystem and transmits this information to the fire visualization subsystem for displaying. Furthermore, the multi-stage fire alarm subsystem sends warnings or alarms based on the fire’s severity. Moreover, the escape route optimization subsystem dynamically optimizes the evacuation routes by considering the actual number of people at the construction site and the potential crowding as people pass through the escapeway. Results show that this system can provide informative and on-time fire protection measures to different participants at the construction site. This study can also serve as a solution to improve fire safety management at the construction site.

To improve the dust control efficiency of soil for backfilling at construction sites, a novel waterborne polymer was used as a dust suppressant, and the dust emission model was created to control the effect of a large-scale field. The results showed that the waterborne polymer could improve the water retention efficiency of soil for backfilling, and the average water content was 2.18 times that of the watered samples, significantly delaying water evaporation. The compressive strength of soil for backfilling reached 4.91 MPa and improved the wind erosion resistance of the consolidation layer, effectively resisting wind damage. At a construction site, the waterborne polymer was sprayed on soil for backfilling, and the concentration of PM10 was reduced by 67.41%, confirming the effectiveness for large-scale utilization.

With regard to the topic of digitization for operational site control, it is clear that while there is an acceleration towards digitization of processes, especially complex ones, there are still clear research gaps to be filled. The present study is part of this line of research and aims to respond to the current demand for digital and sustainable innovation of production processes. It, therefore, aims at defining a framework for the management of an order that takes place in real time. In order to improve the quality of information and organizational processes of an order in a project-oriented logic, a framework has been created: Model Smart Management. The framework integrates project management techniques with digital innovation tools and includes a computerized procedure to monitor and control the operational management of the site: SmartApp. In order to allow the interoperability of information, the platform can be used entirely in the cloud. From the processing of data in the planning phase, ad hoc checklists are developed that include a series of activities closely related to each other to ensure a continuity step by step, allowing users to have an immediate control of the project and of the relevant key performance indicators thanks to an advanced and fully customizable reporting. SmartApp, integrated with a cloud computing system, makes computerized and automated the operational control on site, creating a centralized archive of all company data, reducing time and costs for the management of documentation. Moreover, with the omnichannel system, paper documents are managed and archived through QR-code, optical character recognition on smart devices, so as to achieve a regime of maximum efficiency. This framework was then applied in a case study located in L'Aquila, Italy, represented by a post 2009 earthquake demolition and reconstruction site.

Many industrial accidents occur at construction sites. Several countries are instating safety management measures to reduce industrial accidents at construction sites. However, there are few technical measures relevant to this task, and there are safety blind spots related to differences in human resources’ capabilities. We propose a deep convolutional neural network that automatically recognizes possible material and human risk factors in the field regardless of individual management capabilities. The most suitable learning method and model for this study’s task and environment were experimentally identified, and visualization was performed to increase the interpretability of the model’s prediction results. The fine-tuned Safety-MobileNet model showed a high performance of 99.79% (30 ms), demonstrating its high potential to be applied in actual construction sites. In addition, via visualization, the cause of the model’s confusion of classes could be found in a dataset that the model did not predict correctly, and insights for result analysis could be presented. The material and human risk factor recognition model presented in this study can contribute to solving various practical problems, such as the absence of accident prevention systems, the limitations of human resources for safety management, and the difficulties in applying safety management systems to small construction companies.

Off-site construction is becoming more popular as more companies recognise the benefits of shifting the construction process away from the construction site and into a controlled manufacturing environment. However, challenges associated with the component supply chain have not been fully addressed. As a result, this study proposes a model for three-echelon supply chain supply management in off-site construction with stochastic constraints. In this paper, multiple off-site factories produce various types of components and ship them to supplier warehouses to meet the needs of the construction sites. Each construction site is directly served by a supplier warehouse. The service level for each supplier warehouse is assumed to be different based on regional conditions. Because of the unpredictable nature of construction projects, demand at each construction site is stochastic, so each supplier warehouse should stock a certain number of components. The inventory control policy is reviewed regularly and is in (R, s, S) form. Two objectives are considered: minimising total cost while achieving the desired delivery time for construction sites due to their demands and balancing driver workloads during the routeing stage. A grasshopper optimisation algorithm (GOA) and an exact method are used to solve this NP-hard problem. The findings of this study contribute new theoretical and practical insights to a growing body of knowledge about supply chain management strategies in off-site construction and have implications for project planners and suppliers, policymakers, and managers, particularly in companies where an unplanned supply chain exacerbates project delays and overrun costs.

Researchers have proposed several forms of beacon sensor-based hazard alarm systems for increasing construction workers’ awareness of site hazards, but research on how to deploy beacon sensors so that the system is adequate for achieving timely individualized hazard alarms is scarce. Against this background, this research investigates the impact of different beacon sensor locations in a construction site on how quickly a worker can receive the individualized hazard alarms. This research took an experimental study approach to address this objective. After a prototype of a beacon-based hazard alarm system was developed, the system was tested in a concrete structure building under construction. In the experiment, the locations where the experimenter received the first hazard alarm were recorded in repetitive trials while the beacon sensor was located in four different locations, such as (1) at the entrance of the room, (2) behind the front side wall, (3) on the internal wall facing the access point, and (4) on the internal wall not facing the access point and in a partially enclosed room in the concrete structure. The rate of successful alarm notification (i.e., the rate that the person received the hazard alarm before arriving at the target location) was 89%, 68%, 48%, and 19%, respectively, for the four locations of the beacon sensor. Meanwhile, the heat maps indicating where the hazard alarm notification was received show that the “behind the front side wall” setting yielded the most desired pattern of notification reception, wherein the person received the hazard alarm just before arriving at the room. These results show that the hazard alarm function of the system could be severely affected by the beacon sensor’s location and implies that the locations of beacon sensors should be decided carefully based on the type of hazard involved and the workers targeted for receiving the alarms.

The objective of this study is to evaluate the feasibility of deep-learning-based segmentation of the area covered by fresh and young concrete in the images of construction sites. The RGB images of construction sites under various actual situations were used as an input into several types of convolutional neural network (CNN)–based segmentation models, which were trained using training image sets. Various ranges of threshold values were applied for the classification, and their accuracy and recall capacity were quantified. The trained models could segment the concrete area overall although they were not able to judge the difference between concrete of different ages as professionals can. By increasing the threshold values for the softmax classifier, the cases of incorrect prediction as concrete became almost zero, while some areas of concrete became segmented as not concrete.

The utilization of geopolymer 3D printing for construction products in recent years has been exceptionally substantial, owing to their low carbon dioxide emissions, high-performance properties such as durability, and good thermal and mechanical properties. This automated manufacturing process reduces the need for additional formworks, capital investments, and human resources. Geopolymer 3D printing development is emerging because of its advanced use in construction applications. However, high costs of the initial stages of geopolymer production and 3D printing has inhibited the development of this technology in many countries. This research presents a comprehensive economic evaluation of the investment for each principal stage that facilitates a better deployment of the resources. The study investigated all phases of geopolymer production, from the extraction of raw materials to printing. The cost for the four fundamental stages, namely raw material availability and transportation, pretreatments for raw materials, parameter selection and strength requirements, and printing in factories and on construction sites, were analyzed. The results show that 3D printing of a geopolymer on a construction site is economically more advantageous compared to printing in the factory. The study also verified that raw material transportation cost has the least effect on the finished product cost, whereas pretreatments of raw material and mixing parameters significantly influenced the ultimate cost of the product. Finally, research work suggested the need for future tasks to make geopolymer 3D printing a viable construction approach.

Even though individual mental and health status largely affects the safety in industrial sites, most studies for preventing industrial accidents are mainly focused on external factors such as regulations, education, etc. In this study, the effect of individual factors on safety (i.e., safety consciousness and safety commitment) was analyzed by collecting brainwave and pulse data at construction sites where industrial accidents have occurred with the highest percentage. The effects of brain stress, concentration, brain activity, and left and right brain imbalance on safety accidents were evaluated through brain wave measurements. In addition, the effects of cumulative fatigue, physical vitality, autonomic nerve health, and autonomic balance were identified through pulse wave measurements. Data were acquired for 180 construction workers at various construction sites, and the workers were classified into three grades according to factors that affected safety accidents at construction sites. Then, the safety consciousness and safety commitment levels of workers corresponding to each grade of the influence factors were evaluated by conducting a questionnaire on safety consciousness and safety commitment. As a result, the characteristics of brain and pulse waves required to improve safety consciousness and safety commitment ability of workers at construction sites were explored.

This article proposes a methodology to measure the productivity of a construction site through the analysis of tower crane data. These data were obtained from a data logger that records a time series of spatial and load data from the lifting machine during the structural phase of a construction project. The first step was data collection, followed by preparation, which consisted of formatting and cleaning the dataset. Then, a visualization step identified which data was the most meaningful for the practitioners. From that, the activity of the tower crane was measured by extracting effective lifting operations using the load signal essentially. Having used such a sampling technique allows statistical analysis on the duration, load, and curvilinear distance of every extracted lifting operation. The build statistical distribution and indicators were finally used to compare construction site productivity.

This study sought to develop a typology model of temporary facility constraints that can be used practically in construction site layout planning (CSLP) automation models. A triangulated methodology (literature review, in-depth interviews, and actual case studies) was used to identify constraints of 11 temporary facilities that are considered mainly in CSLP and to classify them into six constraint types (i.e., dimensional, regional, relocation, non-overlap, inter-facility distance, and visibility constraints) and seven subtypes. In addition, this study proposed computational modeling methods that would allow a computer to judge whether or not the constraints are met by a created construction site layout. This study contributes to CSLP theory by providing a typology model of temporary facilities that can be used in the model-based verification of the created construction site layout and constraint processing condition in CSLP optimization models. This would also help provide efficient, safe, and eco-friendly construction site management, while refraining from experience- and intuition-centered CSLP practices.

Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects.

Stair falls on construction sites are leading causes of fatal injuries, and the dangers for workers are greater because they usually carry heavy loads. Nevertheless, there are very few studies related to stair falls among construction workers. The purpose of this study was to detect near fall incidence during stair descent and analyze the changes in weight bearing and center of pressure. A total of 10 healthy males participated in this study. Three experimental conditions were set up to analyze stair falls: natural descent (E1), weighted descent (E2), and near fall-simulated descent (E3). While walking down the stairs, subjects wore a three-axis accelerometer sensor attached to a Smart Helmet and a pair of textile pressure sensors (insole) placed inside Smart Shoes. The collected data were analyzed for: (1) whole body balance, (2) plantar pressure distribution, (3) head tilt pattern, and (4) conformity between the helmet and insole sensors. The results showed that our proposed smart helmet and smart shoes have relatively good performance in terms of classifying the weight-shifting patterns in the head and the feet during stair descent. The results of this study may be helpful in detecting near falls of workers on construction sites.

A field-scale data collection plan to monitor and evaluate the performance of a sediment basin design was developed and implemented using portable automatic stormwater samplers, flow modules, a rain gauge, and inflow weirs. The design configuration consisted of a skimmer as the primary dewatering device, three coir baffles installed inside the basin, polyacrylamide flocculant blocks and ditch checks in the inflow channel. A sediment basin built on a highway construction site in Franklin County, Alabama, U.S. using the aforementioned design configuration was monitored over 16 rainfall events from 15 November 2011 to 6 February 2012. The basin effectively removed sediments during the early stages of construction when the correct type of polyacrylamide flocculant blocks was used, e.g., 97.9% of sediment removal after a rainfall event on 16 November 2011. It is difficult and challenging to dose sediment-laden stormwater inflow with an exact amount of flocculating agent across all runoff producing events since rainfall is a stochastic variable. Based upon results from this study, it is recommended that a minimum volume of 251.9 m3/ha of contributing drainage area be used to sufficiently size a basin, which is still significantly under-designed for a 2-year, 24-h storm event in the southeast. This paper presents challenges and lessons learned regarding sediment basin design, monitoring, and performance that are beneficial to future studies.

Thanks to the wide diffusion of unmanned aerial vehicles (UAVs), geomatics solutions have actively contributed to the field of safety enhancement and disaster risk reduction, supporting rapid mapping and documentation activities of the damages that have occurred to heritage buildings after natural disasters (such as earthquakes) where, in this scenario it is even more important to plan and execute disaster assessment and response operations in safe conditions. In fact, the planning and execution of technical countermeasures in a seismic emergency response involve higher risks for the safety of the operators as compared with responses related to the activities performed at non-critical construction sites. After an analysis of the state of the art, this study aims to underline the possibilities offered by the use of UAVs for performing safety-related tasks, both at post-disaster and non-critical construction sites. First, a survey has been conducted concerning the main user expectations and characteristics that an ideal UAV platform should have in order to perform safety-related tasks at construction sites that are created following the initial emergency phases. The answers that were obtained have been compared with similar studies retrieved from the literature validating previously conducted research. The legislative context is also considered, as it is an important factor that influences the applicability of these platforms and technology. Along with a method for assessing and mitigating the intrinsic risk of using an UAV, the results of a survey submitted to experts in the field of safety at construction sites is also discussed, intending to identify requirements of the ideal platform and the related user expectations. The results are reported, together with a summary of considerations on the use of these strategies in the analyzed contexts.

The main objective of the system presented in this paper is to provide surveyors and engineers with a new photogrammetry device that can be easily integrated with surveying total stations and a global navigation satellite system (GNSS) infrastructure at a construction site, taking advantage of their accuracy and overcoming limitations of aerial vehicles with respect to weight, autonomy and skilled operator requirements in aerial photogrammetry. The system moves between two mounting points, in a blondin ropeway configuration, at the construction site, taking pictures and recording the data of the position and the orientation along the cable path. A cascaded extended Kalman filter is used to integrate measurements from the on-board inertial measurement unit (IMU), a GPS and a GNSS. Experimental results taken in a construction site show the system performance, including the validation of the position estimation, with a robotic surveying total station, or the creation of a digital surface model (DSM), using the emergent structure from motion (SfM) techniques and open software. The georeferencing of the DSM is performed based on estimated camera position or using ground control points (GCPs).

Fifteen advanced architecture students took part in the teaching intervention designed to train them on how to control the employment conditions of construction workers, especially migrant ones. As the highest authority in the construction site, these future construction site directors are the ones who should take social responsibility inherent to their position. A novel Swiss teaching methodology was successfully implemented as it contributes towards the achievement of the learning goals as well as the participants’ behavioral changes. The overall mark of the course awarded by the participants was 9.73/10. The impact of this intervention transcended the classroom and prompted the development of training programs in the construction site and the integration of new contents to the curriculum of the construction site directors training course.

A real-time location (RTL) system was developed to improve safety for multiple laborers in confined construction sites. The RTL system can monitor the location and movement of multiple laborers in real time. A portable RTL system with a low-battery mode was developed to accommodate various constraints in the construction site. A conventional RTL system that uses radio signal strength indicators (RSSIs) has high error, so an accelerometer with Bluetooth Low Energy (BLE) was added, and a calculation process is suggested. Field tests were performed for validation in underground construction and bridge overlay sites. The results show that the accelerometer and BLE can be used as effective sensors to detect the movement of laborers. When the sensor is fixed, the average error ranges 0.2–0.22 m, and when the sensor is moving, the average error ranges 0.1–0.47 m.