Search Results (15,427)

Precise dimensional conformity of precast concrete members is critical for structural performance and on-site assembly accuracy, yet conventional manual inspection remains labor-intensive and unable to scale with modern production-line throughput. Existing scan-vs-BIM approaches address geometric verification in principle but are constrained by manual registration dependencies, the absence of machine-readable IFC-linked tolerance criteria, and limited validation under real factory yard conditions. This study presents a production-scale automated shape quality control (SQC) framework that closes all three gaps simultaneously. A purpose-designed two-point target device enables fully automated, repeatable registration seed-point extraction. A formal IFC property-set-linked rule engine architecture—comprising entity extraction, deviation computation, rule interpretation, and pass/fail decision stages—replaces ad hoc script-based tolerance checking with an interoperable, auditable compliance pipeline. Factory-scale validation on precast arch segments (n = 10) and wall panels (n = 12) achieved registration RMSE of 1.25–1.95 mm, pass rates exceeding 91%, and a 37.1% reduction in inspection time versus manual methods (95% CI: 34.5–39.6%; p < 0.001; Cohen’s d = 3.89). Repeatability testing yielded ICC = 0.971 and Bland–Altman limits of agreement of [−0.45, +1.07] mm. The framework represents a substantive step toward fully digital, production-integrated quality management for industrialized precast construction. Full article

Higher construction costs (CCs) linked to carbon reduction methods have hindered the adoption of low-carbon approaches in the built environment. The simultaneous minimisation of upfront embodied carbon (EC) and CCs has not received much attention in building design optimisation (BDO) research; most studies prioritise operational energy, operational carbon, and operational cost reduction. This paper develops an integrated conceptual framework for low-carbon, cost-effective BDO, particularly targeting upfront EC and CCs, to fill this research gap and meet industry demands. A systematic literature review was conducted following PRISMA guidelines, synthesising 41 peer-reviewed articles published between 2015 and 2026. Thematic and content analyses were employed to extract and categorise key methodological components, including optimisation problem characterisation, objective-driven design variable selection, constraint modelling, algorithm selection, and evaluation and validation approaches. Subsequently, the developed conceptual framework was validated through semi-structured expert interviews with participants comprising BDO researchers and building designers in the construction field. A cross-mapping of optimisation objectives, optimised parameters, and design variables was developed to clarify their interrelationships, alongside structured criteria for optimisation algorithm selection. Based on these insights, a conceptual framework named “ICCO-BD (Integrated Upfront Carbon and Construction Cost Optimisation for Building Design) framework” is proposed and validated, integrating problem formulation, parametric modelling, multi-objective optimisation, and systematic Pareto-based evaluation into a coherent end-to-end workflow, enabling improved time efficiency through reduced redesign iterations, enhanced solution quality via better pareto front exploration, and more robust decision-making through clearer trade-off interpretation. While expert feedback indicated strong conceptual relevance and practical applicability, the framework remains conceptual in nature and requires further empirical verification through real-world case studies and optimisation applications before broader industry implementation. Full article

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

As the rapid development of the industrial Internet of Things (IIoT) progresses, some data in the IIoT start to present the following characteristics: huge volume, high dimensions, distributed storage across multiple devices, and restricted data sharing due to privacy protection concerns. Such data presents a significant challenge to existing data processing methods. To this end, this work proposes an intelligent distributed-data processing method with privacy protection for IIoT (I2DPM). In this method, a federated feature integrator is first designed to capture the global feature subset of the distributed data under privacy protection. Based on the captured feature subset, a many-objective feature selection model is constructed by including the feature number, feature cost, cross-entropy, accuracy, and recall as the five objectives, where these five objectives represent the key factors influencing the feature selection performance. Then, an feedback-assisted information clustering many-objective evolutionary algorithm (MaOEA-IFC) is developed to solve the constructed model and thus obtain the optimal feature subsets, which fully utilizes the ideas of feedforward and feedback control. Finally, MaOEA-IFC is first compared with five state-of-the-art methods on two benchmark test suites to validate its ability to obtain reliable experimental results, and then our method is tested on eight datasets. Extensive results demonstrate that MaOEA-IFC is highly competitive, and our method can obtain the feature subsets with good comprehensive performance on the premise of protecting data privacy. In summary, this work provides a method for processing the data with the above characteristics in IIoT. Full article

Rising heatwaves threaten urban sustainability, necessitating a shift toward heat resilience. This study examines 38 cities across China’s three major coastal urban agglomerations (2016–2024) to quantify dynamic resilience responses. Utilizing a dual-threshold identification method and the Baidu Search Index to construct a Standardized Stress Index (SSI), the research evaluates urban heat vulnerability (UHV) through an exposure–sensitivity–adaptive capacity framework while applying NMF and machine learning models (XGBoost/SHAP) to analyze spatiotemporal heterogeneity. The results show that heatwave pressures peaked in 2022–2023, with Jing–Jin–Ji’s UHV evolving from localized clusters toward regional homogenization. Regional UHV profiles reveal that Jing–Jin–Ji is constrained by population pressures, the Yangtze River Delta (YRD) by resource allocation, and the Pearl River Delta by industrial attributes; notably, the YRD’s systematic coordination effectively offsets structural vulnerability. Furthermore, the optimized XGBoost model achieves strong predictive performance (R² = 0.673), revealing that core factors like summer heat exposure intensity (SHE, 25.65% importance) trigger sharp non-linear surges in social stress upon crossing critical inflection thresholds (e.g., SHE at −0.10). The conclusion will lead to the formulation of differentiated, forward-looking climate adaptation strategies to enhance urban resilience across major regions. Full article

Against the background of profound transformation within the construction industry, the construction industry ecosystem serves as a vital vehicle for regional economic development. Its resilience has become a key factor in influencing the sustainable development of industry. From an ecological perspective, this paper integrated the cusp catastrophe model and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method, using data from Shandong Province to investigate the evolutionary state of the construction industry ecosystem and the diverse concurrent paths driving the system toward high levels of functionality. This study found that: (1) the resilient development of the construction industry ecosystem in Shandong Province presented a differentiated pattern, characterized by dual-core leadership, relative strength in the east, and weakness in the west, and localized catch-up. (2) The results from the cusp catastrophe model indicated that construction industry ecosystems in different regions were primarily undergoing stable evolution, though some cities faced the risk of functional degradation due to the combined effects of insufficient resilience and severe shocks. (3) fsQCA identified three equivalent configuration paths for achieving high system functionality: the “resilience accumulation path”, “resilience synergy path”, and “resilience transition path”, as well as three equivalent configuration paths for low system functionality: the “low resilience-low shocks dependency path”, “low-resilience, fixed-type path”, and “low resilience-high shocks imbalance path”. (4) These paths demonstrated that recovery was the key factor determining a system’s level of functionality. Full article

To address the demand for lightweight, high-performance Al-Si-Mg alloys in aerospace and automotive industries, this work proposes a novel synergistic strengthening strategy by combining rare-earth Y microalloying and in situ synthesized ZrB₂ nanoparticles to construct a hybrid reinforcement architecture. The effects of Y-ZrB₂ additions on the microstructure, crystallographic orientation evolution, and mechanical properties of Al-Si-Mg alloys were systematically investigated via XRD, SEM, EBSD, and tensile/hardness tests. Results show that compared with the base alloy and single-modified alloys, the co-addition of Y and ZrB₂ simultaneously enhances mechanical properties and optimizes grain structure. The optimal comprehensive performance is achieved at 0.3 wt.% Y + 2 wt.% ZrB₂ after T6 heat treatment, with ultimate tensile strength of 332.87 MPa, yield strength of 271.35 MPa, elongation of 16.24%, and Vickers hardness of 153.9 HV. Phase analysis and SEM-EDS confirm a synergistic coupling relationship between Y-rich phases and ZrB₂ nanoparticles. EBSD characterization reveals that Y-ZrB₂ modification has negligible effect on the morphology and crystallographic orientation stability of primary α-Al grains, but effectively regulates the lattice rotation, texture redistribution, and growth behavior of eutectic Si. At the optimal composition, the fraction of high-angle grain boundaries (HAGBs) reaches a maximum of 34.3%. Furthermore, the synergistic effect significantly increases the geometrically necessary dislocation (GND) density and reduces the Schmid factor of the dominant {111}⟨110⟩ slip system, thus enhancing dislocation strengthening and plastic deformation resistance. This work clarifies the intrinsic strength-ductility synergy mechanism of Y-ZrB₂ co-modified Al-Si-Mg alloys, paving a new pathway for the development of advanced lightweight aluminum alloys. Full article

Triethylamine (TEA), a widely used volatile organic compound (VOC), poses severe threats to environmental safety and human health upon accidental leakage, making the development of high-performance TEA detection techniques urgently needed. Herein, we report a Sn-based metal–organic framework (Sn-MOF) constructed from 4,5-dichloroimidazole ligands synthesized via a solvothermal approach. The resulting MOF-derived SnO₂ materials were obtained by calcination at 400–600 °C, yielding SnO₂ with tunable specific surface area and surface defect-site density. Structural and surface characterizations revealed that the materials consist of primary nanoparticles in the range of 10–50 nm, forming aggregated particles of 1–2 µm. The gas sensing performance toward TEA was systematically evaluated. The SnO₂-400 °C sensor exhibited the highest response (S = 85.0) to 100 ppm TEA at 190 °C, with a low detection limit of 1 ppm, superior selectivity, good repeatability, and excellent long-term stability. The observed performance variation was attributed to the combined effects of specific surface area, abundant defect-associated surface sites, and suitable mesoporous structure. This work not only provides a high-performance TEA sensor for industrial and food safety monitoring but also offers a rational strategy for designing MOF-derived metal oxide gas sensors with tailored microstructures and surface defect chemistry. Full article

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

Quinoline derivatives constitute a privileged class of nitrogen-containing heterocycles with extensive applications in medicinal chemistry, agrochemicals, materials science, and functional organic materials. Owing to their broad biological and industrial relevance, the development of efficient, selective, and sustainable synthetic methodologies for quinoline construction remains an active area of research. This review provides a comprehensive overview of recent advances in quinoline synthesis, with particular emphasis on catalytic strategies aligned with the principles of green and sustainable chemistry. Classical transformations, including the Friedländer, Skraup, and Povarov reactions, are revisited in the context of modern catalytic developments that improve reaction efficiency, substrate scope, selectivity, and environmental compatibility. Special attention is devoted to homogeneous and heterogeneous catalytic systems based on both platinum-group and earth-abundant transition metals, highlighting the growing importance of borrowing-hydrogen and acceptorless dehydrogenative coupling methodologies. Recent progress in nanocatalysis, photocatalysis, multicomponent reactions, ionic-liquid-mediated transformations, and metal-free protocols is also critically discussed. Furthermore, solvent-free processes, microwave-assisted synthesis, and recyclable catalytic systems are examined as practical approaches toward minimizing waste generation and energy consumption. Mechanistic aspects, catalytic design principles, substrate limitations, and sustainability metrics are evaluated throughout the review to provide a critical perspective on current methodologies. Collectively, the advances summarized herein demonstrate the rapid evolution of quinoline synthesis toward more atom-economical, environmentally benign, and operationally efficient processes, while also identifying future opportunities for the development of next-generation catalytic platforms for quinoline-based heterocycle construction. Full article

The positioning and clamping process in aircraft assembly exhibits pronounced long-term temporal correlations and intense human–machine interactions. Consequently, assembly quality depends heavily on operator compliance and consistency. Capturing long-term, strongly correlated features in complex industrial environments remains a significant challenge. To overcome this, this study proposes a Long-Term Strongly Associated Action Recognition Network (LTSA-Net) tailored for aircraft assembly positioning and clamping tasks. Based on the C3D backbone, the model first incorporates the SimAM attention mechanism and BN modules to significantly enhance focus on critical spatiotemporal features. To address the challenge of capturing long-term temporal dependencies, LTSFEM is designed to extract global temporal information accurately. Furthermore, to balance structural lightweight design with real-time inference requirements, the CWSTB module is integrated to achieve substantial parameter compression. In addition, a dedicated aircraft assembly positioning and clamping dataset was constructed, and a robust training framework was established using the AdamW optimizer and Mixup data augmentation. Experimental results demonstrate that LTSA-Net achieves a recognition accuracy of 98.82% on the LTSA-Dataset, with a per-frame inference time of 42 ms, successfully meeting the dual requirements of high precision and real-time performance in industrial scenarios, and providing a practical technical solution for intelligent monitoring of aircraft assembly processes. Full article

This study designed and developed a Portable Drill and Grinder Holder with an Integrated Measurement Guide to improve stability, safety, and accuracy in hand-held power tool operations. Addressing workshop challenges like excessive vibration and uncontrolled tool movement, the project employed a developmental research design involving sixteen (16) welding experts. The prototype was constructed using durable, locally available materials to ensure affordability. Evaluation results showed significant improvements in operator control, with Safety receiving the highest rating (M = 3.66). The findings confirm that the tool meets industry standards for instructional and workshop use. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

Accurate detection of internal defects in rolls is crucial for industrial safety and product quality. Ultrasonic testing is a mainstream non-destructive method widely used for this purpose. However, in practice, ultrasonic echo signals often suffer from background clutter. When defects are located near the surface, weak defect echoes tend to couple with surface echoes, making signal extraction difficult and reducing the accuracy of subsequent feature extraction and classification. This paper proposes a novel ultrasonic signal preprocessing method aimed at improving the performance of subsequent defect identification models. The method first acquires ultrasonic signals from defect regions and background clutter reference signals from defect-free regions using a digital ultrasonic flaw detector. An improved median filter is then applied to remove spike interference and boundary outliers. On this basis, a multi-stage FIR (finite impulse response) filter is constructed, and particle swarm optimization is employed to adaptively optimize filter parameters, achieving an accurate estimation of background clutter. Finally, the clutter-suppressed defect signal is obtained through signal subtraction. Experimental results on a dataset of 5000 samples (2500 defective, 2500 non-defective) containing cylindrical artificial defects (diameter 8 mm, length 30 mm) demonstrate that using a CNN classifier with the same feature extraction and classification model, the signals preprocessed by the proposed method outperform traditional median filtering and wavelet denoising methods. The defect identification accuracy is improved by approximately 38 percentage points compared to median filtering and 20 percentage points compared to wavelet denoising, while also achieving a high recall rate, validating the effectiveness of the proposed method in enhancing roll internal defect detection. Full article

Aluminum is undoubtedly a key material in modern industry. Flat plates made of aluminum alloys are widely used in construction, aeronautics, automotive, and others. The current paper presents an analysis of the behavior of a thin plate made of Al7075-T651 aluminum alloy, subjected to a uniaxial stress, and clamped at one end. The results of the numerical simulation with FRANC2D software have been used for accurate determination of the stress intensity factors (K_I, K_II) and being validated for the simple cases using analytical calculations. The Failure Assessment Diagram (FAD) based on the toughness ratio Kr and the load ratio Lr has been used to evaluate the structural integrity of cracked components based on the load, its position, crack size, and the fracture properties of the material. The FAD analysis results highlight the significant influence of crack position on the values of the K factor. The edge and inclined cracks lead to increases in stress intensity factors and to the occurrence of mixed-mode loading conditions. The study demonstrates the effectiveness and usefulness of the proposed methodology in the analysis of structures with discontinuities and emphasizes the importance of crack positioning in assessing the safety of engineering components. Full article