Abstract in English:

Abstract A physics-informed machine learning framework is developed to predict temporal force–displacement responses and three-dimensional deformation histories of hexagonal crash boxes under multi-angle loading. To reduce the computational cost of nonlinear explicit finite element simulations, a three-dimensional convolutional autoencoder (3D-ConvAE) encodes crash box geometries into a low-dimensional latent representation, while a long short-term memory (LSTM) network predicts their temporal deformation evolution. A physics-informed loss function incorporating kinematic admissibility and energy conservation is introduced to suppress non-physical responses and improve mechanical consistency. Validation against benchmark numerical data demonstrates high predictive accuracy, with mean errors of 7.24% for peak force and 6.31% for energy absorption. The framework also maintains geometric fidelity across complex failure transitions, including the shift from progressive folding to global bending, achieving a mean Chamfer distance of 4 mm. The proposed approach provides a robust and computationally efficient tool for predicting nonlinear crash responses and supporting crashworthiness optimization.

Abstract in English:

Abstract Circular holes influence stress fields and raise local stresses in finite width components. This work is devoted to the analytical modeling and the finite element analysis of a rectangular plate with a central circular hole under uniaxial tension. Stress-concentration data from a classical design curve were fitted to a third-order polynomial by ordinary least squares and combined with the net-section nominal stress to produce a dimensionless maximum-stress model that accounts for local amplification and ligament reduction. The formulation recovers the Kirsch limit for a plate of infinite width and predicts an asymptotic rise in stress as the remaining ligament approaches zero. The stationary points were classified according to their mathematical and physical admissibility. The physically admissible stationary points were x=0.0195 (normalized-stress-only case) and x=0.2158 (equal-weight case) which were both treated as objective-function-dependent geometric indicators rather than universal optima. Analytical–numerical differences were in the range of 1.40–3.65% with a mean of 2.69% from Abaqus/CAE simulations using locally refined CPS8R plane-stress elements.

Abstract in English:

Abstract Prestress force and bending rigidity are coupled in prestressed concrete beams, while prestress-force information is weak in deflection responses. This study proposes an incremental second-order-effect-guided physics-informed neural network (iSOE-PINN) for sequential estimation. A first-order model identifies an equivalent bending rigidity, which is transferred to a second-order PINN for prestress-force estimation. Increasing load scales displacement but does not change the nondimensional axial-force parameter or normalized second-order contribution; any benefit therefore arises from measurement SNR rather than physical decoupling. Independent tests give maximum errors of 0.6% for rigidity and 6.3% for prestress force under 1% Gaussian noise. A 0.26% rigidity-transfer bias predicts an 11.3% prestress-force error, consistent with the sequential result. Across 10 initializations, the mean prestress-force error is 10.89% (SD 0.81 percentage points; 95% CI 10.31%-11.47%). Nine experimental cases yield errors of -3.3% to 3.3% for rigidity and -39.2% to 29.5% for prestress force. The method is therefore a preliminary baseline rather than a field-ready diagnostic tool.

Abstract in English:

Abstract Modular cross-laminated timber (CLT) construction offers rapid, low-carbon housing, yet its seismic viability is governed by connection performance. This PRISMA-based systematic review of 50 publications compares the mechanical characterization of twenty connection systems for volumetric CLT across the complete load path, intra-module (InMC), inter-module (IMC) and module-to-foundation (MFC), and sets it against reusability. The evidence separates two families. Adhesive-free, timber-only interlocking joints support circularity but dissipate energy through irreversible timber crushing, so their cyclic ductility is limited and non-repeatable, whereas hybrid steel-timber systems and friction dampers concentrate damage in replaceable components and give stable, repeatable hysteresis; together they define a damage-avoidance hierarchy. Full-scale validation remains scarce, durability unquantified, and the available data biased toward moderate-seismicity codes. The way forward is strong fastener-weak connector design, with stiffness-compatibility guidelines that treat InMC, IMC and MFC as one system.