Search Results (1,403)

This study addresses the common challenges of complex soil behavior and the difficulties in achieving precise control during the construction of large open caissons. A centrifugal model test was conducted to investigate open caisson–fine sandy soil interaction, and the findings were further verified through field testing. Results indicated that during the sinking process, the open caisson–soil interface exhibited slip failure characteristics, while the soil at the cutting edge of the open caisson showed a tendency for inward shear slippage. The horizontal earth pressure along the open caisson sidewall was found to correspond to static earth pressure in the upper section and gradually approached active earth pressure in the lower section. The maximum earth pressure occurred at approximately three-quarters of the embedded depth of the open caisson wall. Furthermore, the friction angle at the soil-open caisson interface was approximately 0.63 times that of the soil friction angle. Based on the observed distribution patterns of earth pressure and skin friction, theoretical calculation formulas were developed. Their accuracy was confirmed through field tests, providing valuable references for the design and construction of large open caisson projects. Full article

Focusing on the construction of a 58-m-diameter double-layer steel space frame dome at the Han Culture Museum Assembly Hall, this study addresses scheme selection and safety control challenges in staggered jacking of large-span spatial structures. A three-dimensional finite element model in MIDAS Gen simulated the three-stage jacking process to compare three temporary support layouts. Numerical evaluation metrics included maximum vertical displacements, peak internal forces, the proportion of members undergoing stress state transitions, and spatio-temporal evolution of stress concentrations. Scheme B demonstrated superior performance, reducing peak vertical displacement by 44% under critical conditions, lowering peak stresses, and enabling more uniform internal force redistribution—effectively mitigating tension–compression cycling and buckling risks. Crucially, only nodal displacements and support elevations were monitored in situ using a 3D system based on magnetic prisms and total stations; no strain or force measurements were conducted due to practical constraints during construction. Monitoring data show good agreement with simulated displacements and support elevations under Scheme B, validating the model’s deformation response. However, localized deviations—including a 29 mm deflection discrepancy and elevation errors up to 28 mm—reveal the influence of uneven boundary conditions, with potential implications for long-term structural behavior. The findings confirm that numerical predictions of deformation are reliable, while internal forces remain unvalidated by field data. The integrated approach of “scheme comparison–construction simulation–full-process displacement monitoring” proves effective for safety control and decision-making in complex jacking operations, offering a transferable framework for similar large-span double-layer space frame projects. Full article

The operational environment of construction machinery is predominantly unstructured, characterized by rapid changes, high complexity, and irregularly distributed objects. This poses significant challenges for 3D semantic perception, particularly due to the high cost of acquiring point cloud semantic labels. To address this, a novel 3D semantic perception scheme is proposed for such unstructured environments. This scheme integrates image semantic segmentation results with point cloud clustering via perspective projection. The projection parameters are refined using Particle Swarm Optimization (PSO), and the semantic consistency of the fused results is further enhanced by a Kd-tree-based radius nearest neighbor (RNN) matching algorithm. Consequently, a weakly supervised framework is established that achieves accurate 3D semantic understanding using only 2D image labels, eliminating the need for annotated 3D point clouds. The feasibility and effectiveness of the scheme are validated through a dedicated unstructured scene dataset and real-world testing. Results demonstrate its capability to effectively perceive 3D semantic information and reconstruct target contours, achieving a mean Pixel Accuracy (mPA) of 84.72% and a mean Intersection over Union (mIoU) of 75.85%. Full article

Despite the aesthetic potential of free-form envelopes in large-scale public buildings, geometric interlacing complexity, ambiguous façade boundaries, and constructability translation gaps persist as systemic barriers. This study addresses these challenges through a Design Science Research (DSR) approach, developing a rule-based digital twin methodology that maintains parametric intelligence across the building life cycle. Implemented via a five-layer integrated framework, i.e., geometric, parametric, BIM, coordination, and fabrication, the methodology was validated through a revelatory case study of the Shenzhen Bay Culture Plaza. Results demonstrate 91.2% clash resolution prior to construction, 20.3 million RMB in cost savings (10.8% reduction), and 35.4% schedule compression, while preserving rule-based relationships into operational facility management. The study advances BIM theory by operationalizing life-cycle digital twins for non-standard geometries, offering a replicable framework for future special-shaped construction projects. Full article

Shale gas “sweet spot” prediction serves as a pivotal technical link in shale gas exploration and development, directly governing the efficiency of exploration deployment and the economic viability of development projects. To address the research gap in sweet spot prediction for complex synclinal structures, this study establishes an integrated geology–engineering–economics evaluation framework, incorporating artificial intelligence (AI)-assisted parameter optimization and dynamic weight adjustment. This innovative approach overcomes the inherent limitations of single-parameter and static evaluation methods commonly employed in new exploration areas. Focusing on the Upper Ordovician Wufeng Formation to Lower Silurian Longmaxi Formation shale sequences within the Anchang Syncline of northern Guizhou, a comprehensive geological characterization of shale reservoirs was accomplished through the fine processing of 3D seismic data (dominant frequency: 30 Hz; signal-to-noise ratio: 8.5) and statistical analysis of logging data. Prestack elastic parameter inversion technology was utilized to quantitatively predict key geological sweet spot parameters, including the total organic carbon (TOC) content and total gas content, with model validation conducted using core test data. Coupled with prestack and poststack seismic attribute analysis, engineering sweet spot evaluation indicators—encompassing fracture development, in situ stress, the pressure coefficient, and the brittleness index—were established with well-defined quantitative criteria. By integrating multi-source data from geology, geophysics, and engineering dynamics, a three-dimensional evaluation system encompassing “preservation conditions–reservoir quality–engineering feasibility” was constructed, with the random forest algorithm employed for sensitive parameter screening. Research findings indicate that high-quality shale in the study area exhibits a thickness ranging from 17 to 22 m, characterized by a TOC content ≥ 4%, gas content of 4.3–4.8 m³/t, effective porosity of 3.5–5.25%, and brittleness index of 55–75. These properties collectively manifest the “high organic matter enrichment, high gas content, and high brittleness” characteristics. Through multi-parameter weighted comprehensive evaluation using the Analytic Hierarchy Process (AHP), complemented by sensitivity testing, sweet spots were classified into three grades: Class I (63 km²), Class II (31 km²), and Class III (27 km²). An optimized well placement scheme for the southern region was proposed, taking into account long-term production dynamics and economic assessment. This study establishes a multi-parameter, multi-technology integrated sweet spot evaluation system with strong transferability, providing a robust scientific basis for the large-scale exploration and development of shale gas in northern Guizhou and analogous complex structural regions worldwide. Full article

The “Great Bend” of the Yellow River, a region characterized by the tension between ecological fragility and economic growth, faces dual pressures from physical water scarcity and stringent policy redlines. Traditional allocation models often struggle to operationalize the rigid boundaries of the “Four Determinants” policy (water determines production, city, land, and population) and suffer from computational inefficiencies under high-dimensional non-linear constraints. To address these issues, this study proposes a policy-driven “Four-Determinant, Three-Multiple” (FDTM) rigid constraint optimization framework. First, a multi-level boundary system is constructed based on water-carrying capacity, thereby converting the policy into dynamic interaction constraints among industry, city, land, and population. Second, to overcome potential computational bottlenecks, an Improved Adaptive Cheetah Optimization Algorithm (IA-COA) is developed. By integrating chaos mapping initialization and an adaptive penalty function mechanism, the algorithm exhibits enhanced global search capability and convergence speed within confined search spaces. Using Baotou City as a representative case study, the model simulates scenarios for the 2030 planning horizon. The results indicate that (i) the integration of rigid constraints effectively identifies development bottlenecks, capping projected water demand at 1.075 × 10⁹ m³ and preventing ecological overdraft despite a 5.15% theoretical deficit; (ii) through IA-COA optimization, a balanced trade-off between economic benefits and ecological security is achieved. The comprehensive water supply guarantee rate increased to over 90%, and satisfaction levels for all sectors exceeded 0.8, demonstrating improved allocation efficiency. This study elucidates the marginal transformation mechanism of the water–economy–ecology nexus under rigid constraints and demonstrates the applicability of IA-COA in solving complex basin allocation problems constrained by strict boundaries. It provides a methodological reference for sustainable water management in similar resource-stressed arid regions. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article

During the assembly process of the bolter miner cutting drum, the varying installation postures of the cutting picks result in unique and non-repetitive irregular gaps between the tooth seat bottom surface and the cylindrical rotating surface. Such gaps are constrained by dual-surface geometry and lack batch statistical regularity, making traditional methods such as shim filling, selective assembly, or on-site welding inadequate for achieving high-precision fitting and reliable process implementation. To address this challenge, this paper proposes an automatic design method for compensation bodies based on computer-aided design, realizing a shift from experience dependence to algorithm-driven design. This method transforms the complex dual-surface gap filling problem into a serialized geometric modeling process: first, smooth extrapolation of the tooth seat bottom surface is achieved through a point sequence prediction model based on minimum mean square error; second, surface projection is simplified to boundary curve projection, enabling precise mapping onto the cylindrical surface and generating trimming surfaces; finally, a ruled surface is constructed to integrate the extended surface with the trimming surfaces, automatically generating a compensation body fully adapted to the gap morphology. Case verification demonstrates that this method can automatically and accurately generate compensation bodies that meet dual-surface fitting requirements, significantly improving geometric adaptability and weldability. This research not only resolves a critical technical bottleneck in the assembly of bolter miner cutting drums but also provides a universal and scalable computational framework for the intelligent compensation design of non-repetitive dual-surface gaps in complex equipment. Full article

To address the semantic gap and complex feature entanglement inherent in multimodal emotion recognition, we propose the Dynamic Heterogeneous Graph Temporal Network (DHGTN), an end-to-end framework designed to model dynamic cross-modal interactions effectively. Utilizing a robust backbone of Wav2vec 2.0, VideoMAE, and BERT, we introduce a “Shared Private” subspace projection mechanism that explicitly disentangles emotion common features from modality-specific noise through contrastive learning to ensure strict semantic alignment. Furthermore, our collaborative Dynamic Heterogeneous Graph and Transformer module overcomes static fusion limitations by constructing time-varying graphs for instantaneous associations and employing global attention to capture long-range temporal dependencies. Extensive experiments on the IEMOCAP and MELD benchmarks demonstrate that DHGTN significantly outperforms state-of-the-art baselines, achieving weighted F1-scores of 73.86% and 66.87%, respectively, which confirms the method’s effectiveness and robustness. Full article

Accurate prediction and optimized control of shield tunneling attitude are critical for ensuring tunneling quality and construction safety. In karst and other highly heterogeneous strata, complex geological conditions and construction parameters exhibit significant nonlinear coupling, greatly increasing the difficulty of attitude regulation. To address this challenge, this study proposes a machine learning-based approach for the automatic control of shield tunneling attitude. First, a Tree-structured Parzen Estimator-optimized Light Gradient Boosting Machine predictive model is employed to construct a nonlinear mapping model between construction parameters and shield tunneling attitude. Subsequently, the SHapley Additive exPlanations (SHAP) interpretability model is introduced to identify the core tunneling factors influencing attitude stability. On this basis, the developed predictive model is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework as a fitness function to achieve multi-objective optimization of key construction parameters. Using field data from shield tunneling construction in the karst strata of Shenzhen Metro Line 16, the proposed model achieved prediction accuracies of R² = 0.959 for pitch and R² = 0.936 for roll, outperforming XGBoost, Random Forest, Long Short-Term Memory, and Transformer baselines. SHAP analysis identified the partitioned propulsion thrust, partitioned chamber pressure, cutterhead rotational speed, and advance rate as key parameters influencing attitude. Further, MOEA/D optimization generated a Pareto set of construction parameters, from which the optimal solution was selected using the ideal point method, resulting in reductions of 26.45% and 39.47% in pitch and roll deviations, respectively. These findings demonstrate the feasibility and effectiveness of the proposed method in achieving high-precision prediction and intelligent optimization control of shield tunneling attitude under complex geological conditions, providing a reliable technical pathway for metro and tunnel construction projects. 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

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

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