Search Results (9,225)

Overhead transmission lines are crucial components of power grid construction, and their carbon emissions significantly impact the low-carbon construction of the power grid. This study adopts a cradle-to-gate life cycle assessment (LCA) method, defining the system boundary as the material production, transportation, and construction phases. Using the carbon-accounting software eFootprint and the emission factor method, we calculate and analyze the carbon emissions of a 500 kV double-circuit overhead transmission line project in Shantou, Guangdong Province, and systematically examine the emission characteristics from material production through construction. Results show that the material production phase dominates the carbon emissions of the project, accounting for 99.82% of the total emissions. Among them, conductors (49.41%) and tower materials (37.28%) are the core sources of carbon emissions, with a combined contribution of 86.69%. The findings highlight conductors and towers as key targets for emission reduction through strategies such as optimized material selection, adoption of high-strength lightweight alternatives, and modular construction techniques. However, this analysis has limitations: it is confined to a single subtropical coastal project, relies on industry-average emission factors from the CLCD database (with inherent methodological uncertainties), excludes operational and end-of-life phases, and should not be generalized without regional validation. While the study identifies key emission hotspots and potential mitigation levers, quantitative low-carbon design guidance requires project-specific data and full life-cycle assessment. Full article

Effective clash management is critical to the success of complex construction projects, yet BIM coordinators face severe information overload when modern detection tools generate thousands or even millions of collision reports, making interdisciplinary coordination increasingly difficult. This article presents a conceptual framework for using AI for collision triage in a Building Information Modeling (BIM) environment. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of Artificial Intelligence (AI) methods for post-processing of results, which constitutes the main innovation of this work. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components, offering adaptive, data-driven decision support unlike static rule-based methods. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite ‘AI Triage Score’ indicator, and a module that assigns responsibility to the appropriate trades and process participants. The framework leverages supervised machine learning methods (gradient boosting algorithms, selected for their effectiveness with tabular data) for noise filtering, density-based clustering (HDBSCAN, chosen for its ability to detect clusters of varying densities without predefined cluster count) for clash aggregation, and multi-criteria scoring models for priority assessment. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources. As a conceptual study, the framework provides a foundation for future empirical validation and its limitations include dependency on historical training data availability and the need for calibration to project-specific contexts. Full article

Raster-stereography and Moiré Fringe Topography are widely recognized as effective techniques for surface screening. Traditionally, these methods have been applied in various medical and clinical contexts, such as assessing human body symmetry, analyzing spinal deformities, evaluating scapular positioning, and predicting trunk-related abnormalities. Both techniques have proven to be reliable tools for examining the human body surface and identifying health-related issues. However, in these techniques, line grids projected onto non-uniform surfaces often break or distort, complicating curvature detection. Capturing and digitizing these distortions through photographymeans further reducing accuracy due to low contrast between background and projected lines. In this paper, we present a modified, i.e., dotted-based, approach to Moiré Fringe Topography construction, offering a simpler, more accurate, and efficient method for recording human body surface curvatures. The proposed technique significantly reduces the complexity of the data acquisition process while maintaining precision in surface analysis. A Single-Photon Avalanche Diode (SPAD) image sensor was used to capture the Moiré patterns. Full article

Saudi Arabia’s construction industry is greatly impacted by rising costs and delays, causing project overruns and high financial pressures. In construction, blockchain technology is a decentralized and secure system that promotes transparency, trustworthiness and effective management of project data and transactions. This research is based on the Technology–Organization–Environment (TOE) framework, which develops and tests a conceptual model to investigate how supply-chain management, smart contracts, transparency and traceability, regulatory compliance and building information modeling (BIM) integration influence blockchain technology adoption, with organizational culture as a moderator. Data from 291 professionals in large Saudi contracting firms were analyzed employing a quantitative, cross-sectional design using SmartPLS. Results confirm all hypothesized factors significantly drive blockchain technology adoption. Organizational culture, acting as a key amplifier, positively moderates all relationships. The model explains 71.1% of the variability in blockchain technology adoption. In order to overcome project challenges and meet Vision 2030’s goals, the results present a validated roadmap for Saudi’s construction sector. The findings show that technical investments and promoting a culture of innovation, collaboration across departments and strong leadership are important for adoption blockchain technology. Full article

Fatigue failure is a prevalent concern within structural engineering, often resulting in critical safety risks. The inherent complexity of construction projects leads to structural components experiencing loads of varying amplitudes and diverse load paths. Investigating the fatigue response under variable-amplitude and load path conditions is essential for mitigating catastrophic failures. This study presents multiaxial fatigue testing of HRB335, a widely utilized construction steel, by subjecting it to variable-amplitude and path loading protocols. Comparative analysis of several established fatigue cumulative damage models, such as Miner, Manson, Tensile Factor, and Bilinear, was conducted based on experimental data to evaluate their effectiveness in predicting fatigue damage accumulation under these complex loading scenarios. The results indicated that, for variable-amplitude loading, the Miner, Manson, and Tensile Factor models demonstrated reasonable accuracy in residual life estimation, with minor deviations observed. Conversely, the Bilinear model exhibited greater variability and reduced predictive precision. Under variable load path conditions, the Manson nonlinear model provided the most accurate predictions, followed by the Miner and Tensile Factor models, while the Bilinear model underperformed. Full article

Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extensive research on rockburst mechanisms and mitigation, the causal analysis of individual events remains challenging due to the complex interaction between seismicity, geological conditions, stress redistribution, and operational factors. This study proposes a structured and multidisciplinary methodology for the causal analysis of rockbursts in deep high-stress tunnels. The methodology integrates seismicity analysis, geological and geotechnical characterization, operational assessment, field damage inspection, and hypothesis-driven interpretation to systematically reconstruct the sequence of processes leading to rockburst occurrence. The proposed approach is applied to a rockburst that occurred in 2020 in the Conveyor Belt tunnel (TC) of the Andes Norte Project, El Teniente Division, Codelco (Chile). The event reached a local magnitude of Mw = 1.7 and caused significant damage to tunnel support systems. Results indicate that the rockburst was associated with excavation- and blasting-induced stress redistribution, leading to the activation of a sub-horizontal rupture plane and subsequent damage propagation toward the excavated tunnel. The methodology provides a transparent and adaptable analytical framework for integrating multidisciplinary data into a coherent causal interpretation. Although demonstrated using a competent and brittle rock mass, the framework can be adapted to other deep tunneling projects under high-stress conditions by adjusting the governing parameters according to site-specific geological, geomechanical, and operational characteristics. The proposed approach supports improved understanding of rockburst mechanisms and informed decision-making for seismic risk management in deep underground excavations. Full article

This study systematically evaluated the response mechanisms of water and sediment processes in the Kuye River Basin to climate change and human activities from 2023 to 2053 by integrating multi-source climate scenarios (CMIP5 models), land-use change projections (based on the Markov chain model), and a distributed hydrological model (SWAT model). The results indicate that under the RCP8.5 high-emission scenario, annual precipitation in the basin shows a non-significant increasing trend but with intensified interannual variability. Spatially, precipitation exhibits a pattern of increasing from northwest to southeast, with a marked decadal transition occurring around 2043. Land-use structure undergoes significant transformation, with construction land projected to account for 30.54% of the total basin area by 2050, while grassland and cropland continue to decline. Water and sediment processes display distinct phased characteristics: a fluctuating adjustment phase (2023–2033), a relatively stable phase (2034–2043), and a sharp growth phase (2044–2053). Parameter sensitivity analysis identifies the curve number (CN2) and soil bulk density (SOL_BD) as key regulatory parameters, revealing the synergistic mechanism by which land-use changes amplify climatic effects through alterations in surface properties. Based on the findings, an adaptive watershed management framework is proposed, encompassing dynamic water resource regulation, spatial zoning, targeted erosion control, and iterative scientific management. Particular emphasis is placed on addressing hydrological transition risks around 2043 and promoting low-impact development practices in high-erosion areas. This study provides a scientific basis for the integrated management of water and soil resources in the context of ecological conservation and high-quality development in the Yellow River Basin. The methodology developed herein offers a valuable reference for predicting water and sediment processes and implementing adaptive management in similar semi-arid basins. Full article

Construction management plays a fundamental role in the sustainability of construction projects, as its primary objective is to enhance cost-effectiveness and efficient resource utilization. One of the main challenges encountered at the early stages of a project’s lifecycle, particularly during the planning phase, is the development and agreement of construction schedules among the stakeholders involved. The tools employed for time planning and scheduling during both the planning and construction phases should therefore be capable of modeling complex environments and supporting dynamic updates in response to resource constraints. Petri nets are known for their capability of modeling complex systems, such as resource management. Their use in project management is essential for resource constraint problems. This paper investigates the use of Petri Nets as a tool for the time scheduling of engineering and construction projects. A case study is presented and modeled using Timed Petri nets, enabling dynamic adaptation under time and resource constraints. Through simulation performed with the ROMEO (v3.10.6) software, the study identifies the critical paths and determines the total project duration under various scenarios of sensitivity by adjusting specific project parameters. The results demonstrate the effectiveness of Petri nets in project management and the benefits they offer when used in modeling complex systems, identifying critical activities and calculating resource constraints and time deadlines. Full article

To address the “silo effect” in construction waste management and the inefficiency of resource allocation in large-scale, multi-section engineering projects, this study developed a cloud-based regional collaborative platform for construction waste management. The platform adopts a technical framework based on Java 1.8.0, Spring Boot 2.4.4, and MySQL 8.0.16, and integrates a visual interactive interface. It supports dynamic access, data entry, quality review, and scheduling of construction waste information across multiple sections and projects. Validated through a case study on the Changhu section of the Guangdong Guanshen–Changhu Expressway expansion project, the platform successfully achieved spatial–temporal optimization of 740 thousand cubic meters of diversified construction waste across seven sections. The comprehensive utilization rate of construction waste increased by more than 25%. Practice has shown that the platform effectively promotes carbon emission reduction in earthworks, enhances resource circularity, and provides digital support for construction quality control. This platform presents an innovative informatics-driven approach to construction waste management, serving as a replicable model. Full article

Deep learning has become an important approach for automated software vulnerability detection. However, due to domain shift, existing models often suffer from significant performance degradation when applied to unseen projects. To address this issue, prior studies have widely adopted Domain Adaptation (DA) techniques to improve cross-project generalization. Nevertheless, these methods typically rely on the implicit “project-as-domain” assumption and require access to target project data during training, which limits their applicability in practice. To overcome these limitations, this paper proposes a vulnerability detection framework that combines semantic clustering with ensemble-based Domain Generalization (DG), termed Cluster-based Ensemble Learning for Vulnerability Detection (CEVD). CEVD first performs unsupervised clustering on code semantic embeddings to automatically automatically identify latent semantic structures that transcend project boundaries, constructing pseudo-domains with intra-domain homogeneity. A soft domain labeling strategy is further introduced to model the membership of samples in multiple pseudo-domains, preserving semantic overlap across boundaries. Building upon this, CEVD employs an ensemble learning framework that jointly trains multiple expert models and a domain classifier. The predictions of these experts are dynamically fused based on the weights generated by the domain classifier, enabling effective vulnerability detection on unseen projects without requiring access to target data. Extensive experiments on real-world datasets demonstrate that CEVD consistently outperforms state-of-the-art baselines across different pre-trained backbone models. This work demonstrates the effectiveness of semantic structure mining in capturing latent domains and offers a practical solution for improving generalization in cross-project vulnerability detection. Full article

With the increasing density of urban underground space development, the soil disturbance induced by large-section rectangular pipe jacking poses a significant threat to the safety of underlying subway tunnels. Taking the Lihe Road utility tunnel project in Wuhan, which crosses over Metro Line 4, as the engineering background, a three-dimensional finite element (FE) model was established using Midas GTS NX to simulate the entire pipe jacking process. Field monitoring data from caisson excavation, ground improvement, pipe jacking, and backfill grouting were introduced for validation, enabling a systematic investigation of the influence mechanism of pipe jacking on existing tunnels. In the numerical simulation, the modified Mohr–Coulomb constitutive model was adopted for the soil, and a “portal-type” reinforcement system was introduced. The pipe jacking process was simulated equivalently with a 1.2 m advance per cycle. The results indicate that the ground settlement induced by pipe jacking exhibits a stage-wise accumulation pattern and eventually develops into a stable settlement trough. The vertical settlement of the tunnel follows an evolutionary law of “early occurrence in the near field, delayed response in the far field, and final convergence,” with peak settlements of 2.44 mm and 2.53 mm for the left and right lines, respectively. Ground improvement significantly mitigates soil deformation, reducing the maximum surface settlement from 45.5 mm to 11.1 mm, decreasing the tunnel’s peak vertical settlement by 37%, and reducing horizontal displacement by 64%, thereby effectively suppressing lateral soil extrusion. The proposed closed-loop analysis method of “numerical simulation–monitoring validation–measure evaluation” reveals the spatiotemporal evolution law of soil–tunnel interaction during pipe jacking construction and provides valuable reference for risk control in similar engineering projects. Full article

Cross-project software vulnerability detection must cope with pronounced domain shift and severe class imbalance, while the target project is typically unlabeled. Existing unsupervised domain adaptation techniques either focus on marginal alignment and overlook class-conditional mismatch, or depend on noisy pseudolabels, which can induce negative transfer in imbalanced settings. To address these challenges we propose DAP2ER, a progressive domain adaptation framework that couples adversarial domain confusion with entropy regularization and prototype-guided high-confidence pseudolabel optimization. Specifically, DAP2ER constructs source class prototypes, selects reliable target samples via confidence-aware pseudolabeling, and performs class-conditional alignment by pulling target features toward the corresponding prototypes. A progressive weighting schedule gradually increases the strength of domain and self-training objectives, stabilizing optimization in early epochs. Experiments on two real-world vulnerability datasets demonstrate that DAP2ER consistently outperforms strong baselines, improving the F1-score by up to 21 percentage points and achieving substantial gains in AUC for bidirectional transfer. Full article

In this paper we compare the rotation of a rigid body in the real three-dimensional Euclidean space E³ and its representation in the complex plane (Klein space), on one hand, with the transformation of polarization states of light (SOPs) by the phase-shifters figured in the complex plane and on the Poincaré sphere, on the other hand. Both the Klein space, in classical mechanics, and the Poincaré sphere, in polarization theory, are abstract spaces, whose construction is based on the classical stereographical projection between Riemann sphere and the simple complex plane C¹. They are classical abstract spaces, even if they have been largely used for representing quantum spinorial physical realities too. At the interface of classical/quantum physics persist some misaperceptions about what is intrinsically of quantum origin and nature, and what is imported from the classical domain. In this context we examine some misunderstandings that take place in the field of these spaces. I shall focus on the double angle relationship between the rotation of representative points of the SOPs on the Poincaré sphere with respect to the corresponding rotations of the azimuthal and ellipticity angles of the “form of the SOPs”, at a transformation of state given by a phase shifter. This is a classical result, that is transferred on the sphere from the complex plane, on the basis of the stereographic bijective connection between the points on the sphere and those in the complex plane. In any textbook of quantum mechanics “the double angle/half angle problem” is presented as a pure quantum spinorial one, avoiding its classical face and origin. A quantum spinorial approach, obviously, recovers the classical results, together with the specific spinorial ones, but with regards to the double angle/half angle issue it is superfluous. We shall also briefly examine the classical and quantum spinorial content of what we know today under the global name of Pauli spin matrices. Often in papers or textbooks of physics the results are presented in a mélange in which it is difficult to establish from which point on one needs to appeal to spinorial or quantum aspects. Full article

Government building projects are particularly complex due to their scale and number of end users, which makes construction prices time-consuming and prone to error. Machine learning is recognized for its ability to process large volumes of complex data quickly with high accuracy, but only a limited number of studies have applied Deep Learning in the early construction stage. Therefore, we aimed to evaluate the potential of Deep Learning to predict construction contract prices for government buildings. Factors were identified through a literature review and interviews with eight experts, and data were collected from 300 government construction projects obtained from Thailand’s Electronic Government Procurement (e-GP) database, the national centralized platform for transparent public bidding. By varying the number of parameters, 80 models were developed and tested. The best-performing model had a three-hidden-layer ratio of 128:64:32 with a Quadratic Loss Function, achieving an R² of 0.918 and an RMSE of 2.022. The results showed 14 significant factors, with the top 5 being (1) usable area, (2) number of sanitary wares, (3) number of rooms, (4) height, and (5) number of elevators. Sensitivity analysis was subsequently conducted to enhance the explainability of the model. The findings demonstrate the potential of Deep Learning to enhance the accuracy of determining construction price and support more effective government budget planning and decision making. Full article

Digital transformation in construction enterprises is frequently constrained by risks that are heterogeneous, context-dependent, and described with inconsistent terminology across studies and practice. Prior research has predominantly relied on expert judgment or narrative reviews to summarize such risks, which limits reproducibility and makes it difficult to iteratively expand the indicator set when new evidence becomes available. To address these challenges, this study develops a hybrid risk identification framework that integrates unsupervised topic modeling with structured inductive coding. Latent Dirichlet Allocation (LDA) is employed to extract latent semantic patterns from a systematically screened body of literature on construction digital transformation, consolidating dispersed risk expressions into coherent thematic units. The Gioia methodology is then applied to inductively structure these themes into a hierarchical risk indicator system, ensuring traceability from textual evidence to conceptual indicators and enhancing interpretability for construction management applications. Rather than enumerating isolated risk events, the proposed framework conceptualizes digital transformation risk in construction enterprises as a set of interacting structural conditions that shape risk exposure across project stages and organizational boundaries. By shifting risk identification from event-based listings to a structural and condition-oriented representation, this study provides a transferable foundation for subsequent causal modeling and multi-criteria risk evaluation in construction digital transformation. Full article