Search Results (58)

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human–robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0. Full article

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment. Full article

This study systematically examines the impact of immigration, emigration, and the influx of foreign workers on employment, unemployment, and economic growth in the Republic of Croatia. The primary objective of the research is to understand how these demographic and economic changes shape the labor market and evaluate their long-term effects on gross domestic product (GDP) and wage levels. The methodology used in this study involves a comprehensive analysis of macroeconomic data from Croatia over the past 11 years, focusing on critical indicators such as employment, unemployment, immigration, emigration, foreign workers, wages, and GDP trends. This approach provides valuable insights into how migration patterns influence critical labor market indicators. The findings reveal that immigration and foreign workers significantly impact employment, particularly in stabilizing sectors like tourism and construction. Additionally, a positive correlation was found between the number of foreign workers and GDP growth. In conclusion, the research underscores the importance of immigration and foreign workers in addressing labor shortages and driving economic growth in Croatia. However, targeted policies are needed to ensure the successful integration of foreign workers and the long-term sustainability of the domestic labor market. Full article

Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards. Full article

The productivity of the construction industry is about half that of the manufacturing industry, and the labor shortage in the construction industry is serious; therefore, improving productivity using information and communication technology (ICT) is an urgent issue. In addition, in civil engineering works, the number of projects that handle multiple types of soil and sand is increasing due to the recycling of construction waste soil; thus, traceability management is important to ensure quality. This paper presents a system that uses sensing on soil-transporting dump trucks and ICT to record which soil was piled up where with the aim of improving the efficiency of traceability management in earthwork construction. This system automatically creates traceability data by linking sensing data and data from the compaction management system via an application. This eliminates the need to record and manage the earthwork location, which was previously required manually to create traceability data, and reduces the labor and manpower required for traceability management. The created traceability data are automatically assigned attribute information such as the construction date and soil information; consequently, they can be used to check the construction history in the future. Full article

The construction industry, a major global employer, increasingly relies on migrant construction workers (MCWs) to mitigate labor shortages and enhance cost efficiency. Despite their vital role, MCWs face numerous challenges, including exploitation during recruitment, safety risks, health issues, and difficulties with social integration. Current research into MCWs is dispersed across various disciplines—such as occupational safety, health, and social issues—and lacks a cohesive review of achievements and gaps. To address this, the present study employs bibliometric and qualitative content analysis to evaluate research progress, domains, hotspots, and trends from 2004 to 2024. The dataset, sourced from the Web of Science (WoS), includes 112 publications. The analysis reveals a steady growth in MCWs research, divided into two distinct phases, with significant contributions from 307 authors across 30 countries. The study also examines robust international collaboration and the prominent role of influential research institutions. The research identified ten key areas of focus and engaged in discussion. This comprehensive overview of MCWs research provides valuable insights for future studies and policy development, aiming to enhance conditions for MCWs and inform effective intervention strategies for this vulnerable workforce. Full article

The construction industry faces significant challenges, including environmental sustainability, rising material costs, and a shortage of skilled labor. Digital fabrication technologies offer innovative solutions to address these issues by reducing raw material consumption and waste generation. Among these, 3D printing technologies offer distinct advantages over traditional construction methods, particularly in handling complex geometries. However, the significant environmental impact of cement in 3D printed concrete, due to its high rheological and printability requirements, remains a concern. This study introduces a novel application of 3D printed permanent formwork in the construction of a winder staircase, assessed through an Environmental Life Cycle Assessment (LCA) from cradle to gate. By comparing the environmental impacts of various construction materials and processes, this study highlights the comparative advantages and disadvantages of conventional methods versus 3D printing. The LCA results reveal that traditional production methods, particularly those using plywood formwork, exhibit higher environmental impacts. In contrast, timber formwork performs better than most 3D printed mixtures in terms of Global Warming Potential (GWP), Acidification Potential (AP), and abiotic depletion potential (ADP). The findings of this study underscore the potential of additive manufacturing for sustainable construction, particularly through the use of low-clinker cement in 3D printed formwork, offering a promising pathway towards reducing the environmental footprint of construction activities. Full article

The development of robotic systems for apple picking is indeed a crucial advancement in agricultural technology, particularly in light of the ongoing labor shortages and the continuous evolution of automation technologies. Currently, during apple picking in complex environments, it is difficult to classify and identify the growth pattern of an apple and obtain information on its attitude synchronously. In this paper, through the integration of deep learning and stereo vision technology, the growth pattern and attitude of apples in the natural environment are identified, and three-dimensional spatial positioning is realized. This study proposes a fusion recognition method based on improved YOLOv7 for apple growth morphology classification and fruit position. Firstly, the multi-scale feature fusion network is improved by adding a 160 × 160 feature scale layer in the backbone network, which is used to enhance the model’s sensitivity in the recognition of very small local features. Secondly, the CBAM attention mechanism is introduced to improve the network’s attention to the target region of interest of the input image. Finally, the Soft-NMS algorithm is adopted, which can effectively prevent high-density overlapping targets from being suppressed at one time and thus prevent the occurrence of missed detection. In addition, the UNet segmentation network and the minimum outer circle and rectangle features are combined to obtain the unobstructed apple position. A depth image of the apple is obtained using an RGB-D camera, and the 3D coordinates of the apple picking point are obtained by combining the 2D coordinates in the RGB image with the depth value. The experimental results show that the recognition accuracy, recall and average recognition precision of the improved YOLOv7 are 86.9%, 80.5% and 87.1%, respectively, which are 4.2, 2.2 and 3.7 percentage points higher compared to the original YOLOv7 model; in addition, the average angular error of the apple position detection method is 3.964°, with an accuracy of 94%, and the error in the three-dimensional coordinate positioning of the apple is within the range of 0.01 mm–1.53 mm, which can meet the demands of apple-picking system operation. The deep-learning-based stereo vision system constructed herein for apple picking robots can effectively identify and locate apples and meet the vision system requirements for the automated picking task performed by an apple-picking robot, with a view to laying the foundation for lossless and efficient apple picking. Full article

This study investigates the design and implementation of circular economy (CE) strategies for managing construction and demolition waste (CDW) in the Santiago Metropolitan Region of Chile (SMRC). The research aimed to identify key factors influencing the current and future adoption of CE practices for CDW management related to socio-environmental, technical, financial, and strategic-regulatory aspects, employing the Delphi method to gather expert insights. Findings reveal that the lack of knowledge about sustainable practices and the absence of regulatory frameworks for CDW disposal are the most critical barriers to effective CE implementation. The study recommends enhancing public awareness and environmental education through government and school programs, as well as enacting stricter legislation to combat illegal disposal and encourage sustainable practices and valorization of secondary raw materials within companies. Additionally, it emphasizes the importance of designing projects that prioritize waste avoidance and the development of infrastructure, technology, and processes for efficient material separation and recycling. The research also highlights potential challenges such as stagnation in the adoption of sustainable practices, skilled labor shortages, and limited research and innovation. It underscores the need for a comprehensive approach to CDW management that integrates socio-environmental, technical, financial, and regulatory dimensions to promote sustainability at both regional and global levels. Full article

Concrete is an essential construction material, and infrastructures, such as bridges, tunnels, and power plants, consume large quantities of it. Future infrastructure demands and sustainability issues necessitate the adoption of non-conventional supplementary cementitious materials (SCMs). At the same time, global labor shortages are compelling the conservative construction sector to implement autonomous and digital fabrication methods, such as 3D printing. This paper thus investigates the feasibility of using oil shale ash (OSA) as an SCM in concrete suitable for 3D printing, and collision milling is examined as a possible ash pretreatment. OSA from four different sources was collected and analyzed for its physical, chemical, and mineralogical composition. Concrete formulations containing ash were tested for mechanical performance, and the two best-performing formulations were assessed for printability. It was found that ash extracted from flue gases by the novel integrated desulfurizer has the greatest potential as an SCM due to globular particles that contain β-calcium silicate. The 56-day compression strength of concrete containing this type of ash is ~60 MPa, the same as in the reference composition. Overall, collision milling is effective in reducing the size of particles larger than 10 μm but does not seem beneficial for ash extracted from flue gasses. However, milling bottom ash may unlock its potential as an SCM, with the optimal milling frequency being ~100 Hz. Full article

Unmanned aerial vehicles (UAVs) offer solutions to issues like traffic congestion and labor shortages. We developed a distributed UAV management system inspired by virtual circuit and datagram methods in packet-switching networks. By installing houses with wireless terminals, UAVs navigate routes in a multi-hop network, communicating with ground nodes. UAVs are treated as network packets, ground devices are treated as routers, and their connections are treated as links. Activating all nodes as relays increases control message traffic and node load. To optimize connectivity, we minimize relay nodes, connecting non-relay nodes to the nearest relay. This study proposes four relay node selection methods: random selection, two adjacency-based methods, and our innovative approach using Multipoint Relay (MPR) from the Optimized Link State Routing Protocol (OLSR). We evaluated these methods according to their route construction success rates, relay node counts, route lengths, and so on. The MPR-based method proved most effective for UAV route construction. However, fewer relay nodes increase link collisions, and we identify the minimum relay density needed to balance efficiency and conflict reduction. Full article

Advanced construction techniques, such as additive manufacturing (AM) and modular construction, offer promising solutions to address labor shortages, reduce CO₂ emissions, and enhance material efficiency. Despite their potential, the adoption of these innovative methods is hindered by the construction industry’s fragmented expertise. Building Information Modeling (BIM) is frequently suggested to integrate this diverse knowledge, but existing BIM-based approaches lack a robust framework for systematically documenting and retrieving the cross-domain knowledge essential for construction projects. To bridge this gap, this paper presents an ontology-driven methodology for documenting and utilizing expert knowledge, with a focus on AM in construction. Based on a well-founded ontological framework, a set of modular ontologies is formalized for individual domains. Additionally, a prototypical documentation tool is developed to elevate recorded information and BIM models as a knowledge graph. This knowledge graph will interface with advanced large language models (LLMs), enabling effective question answering and knowledge retrieval. Full article

In recent years, the construction industry has faced challenges related to rising material costs, labor shortages and environmental sustainability, resulting in an increased interest in modular construction cores composed of recycled materials, such as XPS, PUR, PLW and GFRP, from waste from the truck body industry. Two resins, PUR and polyester, were used to bond these recycled composites. Physical, chemical and mechanical analyses showed that the panels formed with PUR resin had superior workability due to the higher open time of the resin, 11.3% better thermal conductivity than the commercial PLW panel (SP-PLW) and reduced porosity compared to those using polyester resin. The mechanical performance of the panels improved with higher structural reinforcement content (PLW and GFRP). Compared to a commercial panel (SP-PLW), the SP-RCM1 recycled panel showed 4% higher performance, demonstrating its potential for sustainable building applications. Thermal and microscopic characterizations showed good adhesion of the materials in the best performing formulations related to higher thermal stability. Therefore, this research aims to demonstrate the feasibility of using waste from the car industry in the manufacture of sandwich panels for modular construction to address these issues. Full article

Amid an aging workforce and labor shortages, this study investigates the key factors influencing construction workers’ safety compliance behavior (SCB). SCB is categorized into three distinct types: non-compliance behavior, general behavior, and compliance behavior. The study compares and analyzes the differences in influencing factors between the new generation and older generation of construction workers. By integrating the SCB framework with a multi-layer perceptron (MLP) model, this research develops a safety compliance behavior–artificial neural network (SCB-ANN) model. An enhanced method for optimizing connection weight (CW) is applied to identify the key determinants of SCB. The findings reveal that the SCB-ANN model offers superior predictive accuracy compared to a standard MLP model. Additionally, the refined CW method significantly improves the neural network’s interpretability. The analysis shows that organizational factors have a stronger influence on the new generation of construction workers (NGCWs), while individual factors play a more crucial role for the older generation (OGCWs). As a result, the study proposes tailored safety management measures for different worker groups to mitigate non-compliance behaviors, providing a robust foundation for future research and the development of safety management strategies. Full article