We propose a Weak-form Physics-Informed Neural Operator (WINO), a data-free framework that combines the efficiency of neural operators with the geometric flexibility of the φ-finite element method (φ-FEM). φ-FEM is an unfitted method that accommodates geometric variations without body-fitted meshes, where the domain geometry is represented by the level-set function φ. To impose the boundary conditions, Dirichlet problems adopt the φ-FEM lifting so only the homogeneous displacement contribution is learned, whereas traction-driven Neumann problems additionally predict the auxiliary fields necessary for the unfitted weak formulation. Parameters are trained by minimizing squared weak-form residuals aligned with φ-FEM together with squared penalties on the cut-cell auxiliary equations, which removes the need for large paired datasets of converged reference solutions. When labeled reference data are available, an optional data-augmented variant (WINO+data) can further combine this physics-informed loss with a supervised term. After training, WINO outputs can seed the nonlinear φ-FEM solvers as neural operator warm starts (NOWS), which reduce iteration counts relative to traditional cold-started solvers. Numerical benchmarks show substantial accuracy of WINO together with total training times of about 15%–70% of those of supervised φ-FEM-FNO across all cases, without requiring reference-solution generation.
Short carbon fiber-reinforced polymer (SCFRP) composites exploit the intrinsic conductivity of the carbon fiber network for self-sensing, yet no predictive model couples their anisotropic, rate-dependent fracture to piezoresistive damage identification. This work presents a finite deformation multiphysics phase-field framework coupling a viscoelastic-viscoplastic constitutive model, an anisotropic crack resistance formulation, and a piezoresistive conductivity model. The three sub-problems are unified through the second-order fiber orientation tensor, which simultaneously defines fiber family directions, crack resistance anisotropy, and principal conduction paths of the carbon fiber network. A damage-coupled conductivity tensor captures both strain-driven geometric-kinematic resistance changes and irreversible network severance driven by the phase-field variable. The framework is coupled to an eight-electrode electrical impedance tomography configuration, and the normalized inter-electrode conductance ratios serve as inputs to a feedforward artificial neural network that infers normalized crack length and mechanical compliance without mechanical sensing. The network achieves R2 = 0.99 on held-out configurations, confirming generalization across the microstructure space. The framework establishes a physics-based, computationally efficient route for real-time structural health monitoring and inverse damage assessment in SCFRP composites.
A phase field model based on an orthogonality-based energy split and incorporating the effect of in-situ stress is proposed to simulate hydraulic fracturing in transversely isotropic rocks under complex stress conditions. The driving force is reconstructed through volumetric–deviatoric decomposition in a transformed strain space, thereby rigorously satisfying the energy orthogonality condition required for tension–compression asymmetry in anisotropic materials. Meanwhile, the influence of in-situ stress field is incorporated into the energy functional. Within the framework of Biot poroelasticity and phase field fracture theory, the proposed PFM is formulated and validated through a series of numerical examples and experimental comparisons. Then the fracture propagation mechanisms under bedding anisotropy, perforation angle, and in-situ stress difference are then systematically discussed. The anisotropy of the critical energy release rate exerts the strongest control on the fracture trajectory, followed by elastic modulus anisotropy, whereas permeability anisotropy primarily governs pore-pressure redistribution and peak pressure response despite its weaker influence on the final fracture path.
Abstract Construction logistics significantly influence the sustainability, safety, and efficiency of surrounding transport infrastructure, for which the public sector bears primary responsibility. Despite this, construction logistics are often insufficiently addressed in planning and building permit processes, leaving governance mechanisms underutilized. Existing research largely focuses on operational solutions, while comparative analyses of regulatory frameworks and their enforcement in urban construction logistics remain limited. This study compares the legal and regulatory frameworks governing construction logistics in Germany and the United Kingdom, as well as their implementation in practice. The analysis is based on a review of relevant political and legal documents, and a comparative assessment of construction logistics plans and manuals using country-specific project examples. The results show that, although overarching policy objectives related to urban logistics are similar in both countries, construction logistics are weighted differently in regulatory practice. Germany relies primarily on project-specific, nonstandardized decisions, whereas the United Kingdom applies a nationally standardized approach to construction logistics planning and permits. These differences lead to variation in the consistency and scope of impact mitigation measures. The findings reveal characteristics of both systems and provide a foundation for improving governance approaches.
The 2023 Kahramanmara & scedil; earthquakes caused unprecedented structural damage across South-Eastern T & uuml;rkiye, highlighting the critical need for rapid post-disaster assessment and understanding the root causes of failure in reinforced concrete (RC) structures. This study provides a comprehensive comparative analysis of 207 RC buildings located in Ad & imath;yaman, Hatay, and Kahramanmara & scedil;. A novel methodological approach was employed by integrating post-earthquake field observations with pre-earthquake digital data obtained via Google Street View to identify structural irregularities and damage patterns. The investigated buildings were classified based on their damage levels, with 11.1% categorized as heavily damaged, 34.3% as to-be-demolished, and 54.6% as collapsed. Significant structural irregularities, including soft stories (ranging from 64.9% to 82.7%), heavy overhangs, and vertical discontinuities, were found to be the primary drivers of severe damage. Furthermore, pounding and short-column effects were identified as the most prevalent damage types across all three provinces. The results demonstrate that pre-existing structural irregularities significantly exacerbated the seismic vulnerability of the RC building stock. This research emphasizes the importance of stringent adherence to design codes and suggests that integrating digital imagery into post-disaster surveys can significantly enhance the accuracy of damage classification for future earthquake resilience.
