Search Results (47)

In recent years, the AEC industry has increasingly sought sustainable solutions to enhance productivity and reduce environmental pollution, with wood emerging as a key renewable material due to its excellent carbon sequestration capability and low ecological footprint. Despite significant advances in digital fabrication technologies for timber construction, on-site assembly still predominantly relies on manual operations, thereby limiting efficiency and precision. To address this challenge, this study proposes an automated on-site timber construction process that integrates a mobile construction platform (MCP), a fiducial marker system (FMS) and a UWB/IMU integrated navigation system. By deconstructing traditional modular stacking methods and iteratively developing the process in a controlled laboratory environment, the authors formalize raw construction experience into an effective workflow, supplemented by a self-feedback error correction system to achieve precise, real-time end-effector positioning. Extensive experimental results demonstrate that the system consistently achieves millimeter-level positioning accuracy across all test scenarios, with translational errors of approximately 1 mm and an average repeat positioning precision of up to 0.08 mm, thereby aligning with on-site timber construction requirements. These findings validate the method’s technical reliability, robustness and practical applicability, laying a solid foundation for a smooth transition from laboratory trials to large-scale on-site timber construction. Full article

The misuse of fentanyl poses significant social risks, and accurately and swiftly detecting fentanyl in field settings presents a considerable challenge. Herein, we have designed and synthesized a fluorescent probe TP-CF₃-COOH, which is composed of carboxyl- and trifluoromethyl-binding center tetraphenyl butadiene. The unique centrosymmetric configuration of the TP-CF₃-COOH probe allows for the construction of a fluorescence “on–off” mechanism recognition platform by spatially matching fentanyl and its metabolite norfentanyl. Importantly, this study reveals that the interaction of fentanyl or norfentanyl with TP-CF₃-COOH results in spontaneous self-assembly, generating a three-dimensional complex sphere that is smaller than the two-dimensional sheet fluorescence probe. This self-assembly process results in the quenching of fluorescence. Theoretical calculations demonstrate that this process is accompanied by intermolecular through-space charge transfer during self-assembly, leading to a blue shift in emission wavelength. As a result, the TP-CF₃-COOH fluorescent probe enables the quantitative detection of fentanyl/norfentanyl within a range of 1 × 10⁻²–1 × 10³ μg/L, with limits of detection of 2 × 10⁻⁴ μg/L and 3 × 10⁻⁴ μg/L, respectively. This cost-effective, rapid, and sensitive fluorescent probe holds great potential for the onsite screening and detection of fentanyl and its analogues. Full article

Grouting technology is widely used in construction and civil engineering, where evaluating grouting effectiveness is crucial due to the uncertainty of subsurface conditions. Existing methods face drawbacks such as destructiveness, high cost, poor durability, and limited data collection. To address these issues, this paper proposes a novel wireless real-time monitoring system based on a ZigBee sensor network framework. The sensor system integrates a direct current method in geophysics with apparent resistivity measurement to assess grouting effectiveness in real time. It consists of multichannel data acquisition units with electrodes for sensing underground currents and a user control unit for centralized management and data processing. A system acquisition performance test confirmed that the differential input channel’s equivalent input noise of the ADC was only 175 μV and 188 μV, and the average error of the captured sine wave data was 4.51 mV and 4.19 mV, ensuring the voltage measurement accuracy of the data acquisition units. Stability testing of the equipment in road and construction environments showed an average RSD of 2.86% and 2.92%, respectively, indicating good stability of the measurements. ZigBee network performance tests in three simulated environments and a field test showed that the packet loss rate (PLR) was less than 2% from 0 to 50 m, ensuring network communication in grouting project scenarios. On-site experiments demonstrate that the system can simultaneously monitor multiple profiles and perform inversions in the grouting area, which can be assembled into 3D inversion images for evaluating grout diffusion, offering valuable insights for optimizing construction operations, and enhancing grouting efficiency. Full article

This work explores issues related to traditional heritage, its evolution, and its transmission within construction practices. It focuses on a case study concerning the reintroduction in Tamentit, an oasis in southwestern Algeria, of a nearly forgotten construction technique: the use of a local stone known as “Agharf”, composed of saline pebbles, bound or assembled with a clay mortar enriched with salt, allowing the construction of robust structures adapted to their environment. Traditionally used in certain specific areas of the Sahara, it was notably employed in isolated regions such as Siwa in Egypt. After a long period of disuse, this technique is experiencing a renewed interest and appears to be gradually reintegrating into the local practices of artisans. This raises several questions: What justifies the return of this technique? What role does contemporary society assign to it, and what actions are being taken to ensure its sustainability? Fieldwork, consisting of on-site observations and semi-structured interviews with artisans and master artisans, the ma‘alem, was conducted to analyze their perception of this heritage, to understand the tangible and intangible aspects of the construction process, and to explore the challenges related to its transmission. The interviews reveal that, despite the challenges and reservations expressed by the community, the Agharf remains for the artisans a symbol of identity and craftsmanship, far from being a lost intangible heritage. The conditions and benefits of its use are also discussed. Full article

3D Concrete Printing (3DCP) technology is rapidly gaining popularity in the construction industry, particularly for transportation infrastructure such as bridges. Unlike traditional construction methods, this innovative approach eliminates the need for formwork and enhances both economic efficiency and sustainability by lowering resource consumption and waste generation associated with formwork. This paper examines current research on 3D-printed concrete bridges, highlighting key areas such as concrete mixtures, design processes, construction techniques, and reinforcement strategies. It delves into computational methods like topology optimization and iterative “design by testing” approaches, which are crucial for developing structurally efficient and architecturally innovative bridges. Additionally, it reviews specific admixtures or additives within the concrete mix, assessing how they improve essential properties of printable concrete, including extrudability, buildability, and interlayer bonding. Moreover, it shows that the primary construction approach for 3DCP bridges involves prefabrication and on-site assembly, with robotic arm printers leading to scalability and precision. Reinforcement continues to be challenging, with the most commonly used strategies being post-tensioning, hybrid techniques, and fiber reinforcement. This paper offers insights into the advancements and challenges in 3D-printed concrete bridge construction, providing valuable guidance for future research and development in this field. Full article

Prefabricated buildings, characterized by factory production, on-site assembly, and efficient and refined management, enhance construction efficiency, reduce building time, and promote material reuse and recycling. The energy consumption (and carbon emissions) during the building operational stage are significantly influenced by the performance of the building envelope component system. To minimize carbon emissions throughout the building’s lifecycle, it is essential to focus on a comprehensive optimization design for carbon reduction in prefabricated envelope systems. This paper draws on grounded theory to construct a system of factors influencing carbon emissions throughout the lifecycle of prefabricated building envelopes. Using a questionnaire survey and leveraging Structural Equation Modeling (SEM), this study identifies key pathways and factors, influencing carbon emissions throughout the lifecycle of building envelope components. It provides insights into carbon emission mechanisms in these components and establishes a comprehensive design pathway for carbon control throughout the lifecycle of building envelope systems. Subsequently, the survey results were analyzed using Structural Equation Modeling (SEM) to identify key factors influencing carbon emissions throughout the lifecycle and their interrelationships. These findings were integrated into the various stages of the whole-process design, yielding actionable recommendations for carbon control in the design process. Additionally, the case study method was employed to illustrate how carbon control design and optimization techniques can be applied at each stage of a specific project, providing a practical demonstration of the research outcomes. The study offers optimized methods for carbon control across the entire process, utilizing optimization strategies to reduce carbon emissions at each stage of the building’s lifecycle. Full article

Prefabricated buildings have become important in the transformation and upgrading of the construction industry due to their advantages, including high efficiency, energy conservation, low cost, and environmental friendliness. To further promote the wide application of prefabricated construction, the improvement of construction organization design has become an urgent problem to be solved. Therefore, this study developed a new evaluation method for prefabricated construction collaboration. The proposed evaluation system was built based on the combination of knowledge- and data-driven approaches, i.e., a dual-driven evaluation method. The knowledge-driven part of this evaluation system used an evaluation model based on the analytic hierarchy process (AHP), while the data-driven part used a prediction model based on the BO-XGBoost algorithm to verify the validity of the AHP-based model. To demonstrate the effectiveness of the proposed dual-driven evaluation system, we conducted a case analysis using the data of 204 construction cases obtained from digital simulation platform experiments. The results of the AHP-based evaluation model showed that there was a significant disparity in construction collaboration levels in this case study, with a large proportion of low-level collaboration cases. This indicated that there was a lack of proper collaboration in project management, component production, and on-site assembly, reflecting the urgent need for improvement in collaboration efficiency. Regarding the data-driven analysis, the BO-XGBoost prediction model was built based on the AHP-based evaluation results. It was found that the prediction accuracy of the BO-XGBoost model was as high as 98.1%, indicating that the proposed AHP-based model was scientific and effective. Moreover, the BO-XGBoost model was compared with the random forest, support vector machine, and logistic regression prediction models. The BO-XGBoost model outperformed the other three prediction models in terms of accuracy, precision, recall rate, and F1 score. The proposed dual-driven evaluation system provided a new perspective for the scientific evaluation of prefabricated construction collaboration. The findings of this study contributed to enhancing the project management optimization capability of smart construction sites. Full article

Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current testing (ECT) is commonly employed during poolside inspections due to its simplicity and ease of on-site implementation. The use of swept frequency technology can mitigate the impact of interference parameters and improve the measurement accuracy of ECT. However, as the nuclear assembly is placed in a pool for inspection, a cable several dozen meters in length is used to connect the ECT probe to the instrument. The measurement is affected by the transmission line and its effect is a function of the operating frequencies, resulting in errors for swept frequency measurements. This paper proposes a method for precisely measuring oxide film thickness based on the swept frequency technique and long transmission line impedance correction. The signals are calibrated based on a transmission line model of the cable, effectively eliminating the influence of the transmission cable. A swept frequency signal-processing algorithm is developed to separate the parameters and calculate oxide film thickness. To verify the feasibility of the method, measurements are conducted on fuel cladding samples with varying conductivities. It is found that the method can accurately assess oxide film thickness with varying conductivity. The maximum error is 3.42 μm, while the average error is 1.82 μm. The impedance correction reduces the error by 66%. The experimental results indicate that this method can eliminate the impact of long transmission cables, and the algorithm can mitigate the influence of material conductivity. This method can be utilized to measure oxide film thickness in nuclear power maintenance inspections following extensive testing and engineering optimization. Full article

The latest policy and research recommendations focus on advancing transition of housing to the circular economy framework to tackle environmental and affordability challenges. A key strategy for this is industrialised construction, which combines controlled manufacturing methods with strategies that facilitate future disassembly, allowing for adaptations, maintenance, and material reuse. Despite its importance, long-term housing solutions that integrate both industrialised construction and disassembly remain rare. This study obtained insights into circular industrialised housing from the Solar Decathlon Europe competition through interviews and observations with fifteen participating teams in Wuppertal, Germany, in 2022. The competition’s build challenge provided a unique opportunity to examine the practical application of both industrialised and disassembly approaches, where teams developed highly energy-efficient, affordability-conscious, and scalable housing systems. On-site interviews with team members from diverse disciplines took place midway through the competition’s assembly phase. These were further informed by observing team Azalea’s housing disassembly in Spain, which took place shortly before reassembly in Germany. Thematic and content analyses were conducted using a predefined framework based on holistic factors and lifecycle processes. Our results reveal the critical impact of Cultural factors, particularly during the (re)design process and provide new data to aid our understanding of the (dis)assembly process. This study contributes towards the development of a circular industrialised housing framework. Full article

In the complex process of assembling biomass heating systems, traditional paper documents and construction process card management methods have weak information correlation and take a long time for information retrieval, which seriously restricts the assembly efficiency and quality. Moreover, the assembly process involves numerous components and complex processes, making it difficult for traditional management methods to cope with. To address this issue, a knowledge graph-based assembly information integration method is proposed to integrate scattered assembly information into a graph database, providing pathways for accessing assembly information and assisting on-site management. The biomass heating system assembly knowledge graph (BAKG) adopts the top-down method construction. After the construction of the upper schema layer, the 3DXML file was parsed, the XML.dom parser in Python3.7.16 was used to extract the equipment structure information, and the RoBERTa-BiLSTM-CRF model was applied to the named entity recognition of the assembly document, which improved the accuracy of entity recognition. The experimental results show that the F1 score of the RoBERTa-BiLSTM-CRF model in entity recognition during the assembly process reaches 92.19%, which is 3.1% higher than that of the traditional BERT-BiLSTM-CRF model. Moreover, the knowledge graph structure generated by the equipment structure data based on 3DXML file is similar to the equipment structure tree, but is more clear and intuitive. Finally, taking the second-phase construction process records of a company as an example, BAKG was constructed and assembly information was stored in the Neo4j graph database in the form of graphs, which verified the effectiveness of the method. Full article

The dimensional quality inspection of prefabricated concrete (PC) elements is crucial for ensuring overall assembly quality and enhancing on-site construction efficiency. However, current practices remain heavily reliant on manual inspection, which results in high operator dependency and low efficiency. Existing Light Detection and Ranging (LiDAR)-based methods also require skilled professionals for scanning and subsequent point cloud processing, thereby presenting technical challenges. This study developed a 3D LiDAR system for the automatic identification and measurement of the dimensional quality of PC elements. The system consists of (1) a hardware system integrated with camera and LiDAR components to acquire 3D point cloud data and (2) a user-friendly graphical user interface (GUI) software system incorporating a series of algorithms for automated point cloud processing using PyQt5. Field experiments comparing the system’s measurements with manual measurements on prefabricated bridge columns demonstrated that the system’s average measurement error was approximately 5 mm. The developed system can provide a quick, accurate, and automated inspection tool for dimensional quality assessment of PC elements, thereby enhancing on-site construction efficiency. Full article

The transition from polylithic (composed of many parts) to monolithic (one part) design in automotive components presents an opportunity for a reduction in part count, weight, processing routes, and production time without compromising performance. The traditional design approaches for rooftop tents assemble various sheet metal and extrusions together using different joining processes such as welding, adhesive bonding, bolting, and riveting. This is often associated with disadvantages, such as increased weight, high production time, and leaking joints. This research, therefore, presents the development of a monolithic, lightweight, stiffened, non-rotational automotive rooftop tent that is manufactured via the deep-drawing process. An onsite company case study was conducted to analyze the polylithic product and its production process to determine its limitations. This was followed by the design of a lightweight, non-rotational monolithic product whose purpose is to eliminate the identified disadvantages. The stiffness geometries were developed to enhance the overall structural integrity without adding unnecessary weight. The Analytic Hierarchy Process (AHP) was used to analyze and evaluate alternative layouts against criteria such as complexity, tool design, symmetry, rigidity, and cost. Simulations conducted using NX 2024 software confirmed the effectiveness of this design. The results show that the monolithic rooftop tent has a comparable stiffness performance between the lightweight, monolithic rooftop tent and the heavy, polylithic rooftop tent. At the same time, the part count was reduced from twenty-three (23) single parts (polylithic) to a one (1) part (monolithic) rooftop tent, the weight was reduced by 15.6 kg, which translates to a 30% weight reduction without compromising the performance, processing routes were reduced from eight (8) to three (3), production time was reduced by 120 min, and leaking was eliminated. It can, therefore, be concluded that the design and manufacturing of monolithic rooftop tents leads to a lighter and stronger product. Full article

This paper proposed an innovative precast steel-reinforced concrete (PPSRC) squat wall to simplify on-site construction. In PPSRC squat shear walls, the hollowly precast RC wall panel can be assembled on-site through the pre-erected steel shapes, and the boundary cores will be filled using fresh concrete together with the slab system. The seismic performance of PPSRC squat walls, influenced by different construction techniques (cast-in-place vs. precast) and steel ratios, was examined through pseudo-static experiments on three specimens. Some key performance indicators, including hysteretic behavior, skeleton curves, stiffness degradation, energy dissipation, and load-carrying capacity, were analyzed in detail. The test results indicated that all the PPSRC squat walls failed in typical shear failure, and no significant slippage between the precast and fresh concrete sections was observed during the loading process, indicating that the composite action could be fully achieved via the novel throat connectors. In addition, the PPSRC squat walls could achieve comparable seismic performance compared with that of cast-in-place SRC shear walls (the peak load of the PPSRC squat wall only increased by 0.26% compared with the control specimen), and the load-carrying capacity and deformability could be enhanced by increasing the steel ratio in the boundary elements. Finally, an elaborate finite element model was developed and validated using ABAQUS software. The parametric analysis of the concrete strengths of precast and cast-in-place parts and the axial load was conducted further to investigate the seismic performance of PPSRC squat walls. Full article

Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge “intelligence” to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet. Full article

The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process. Full article