1663 papers
This study demonstrates that predicting the compression index of central Iraqi alluvial soils is most accurately achieved by combining the Plasticity Index with the initial void ratio in a multi-linear regression model, thereby overcoming the limitations of single-variable approaches and providing a cost-effective alternative to expensive laboratory testing.
This paper proposes an integrated framework that combines an enhanced DeepLabv3+ segmentation model with edge-aware attention and geometric pattern analysis to achieve superior, pixel-level identification and quantitative characterization of rock fractures in complex environments.
This paper proposes a novel POD-assisted Fourier-gradient Physics-Informed Neural Network (FPINN) framework that effectively mitigates spectral bias, mass leakage, and high computational costs to achieve accurate and efficient reconstruction of Navier-Stokes flow fields across various Reynolds numbers.
This study demonstrates that in TRIP800 steel, grain coarsening synergistically enhances sheared edge quality and stretch-flangeability under multi-step blanking by promoting delayed strain hardening and redistributing stress triaxiality to suppress damage accumulation and fracture initiation.
This paper reviews the mechanical engineering aspects of robotics by modeling manipulators and mobile platforms, evaluating the performance of classical and modern control strategies like PID, LQR, and MPC through MATLAB/Simulink simulations, and concluding with their industrial applications and future research directions.
This paper presents a validated semi-empirical model demonstrating that wet arching during the slow consolidation of hydraulic backfills significantly increases the average effective vertical stress at the stope bottom compared to conventional dry-arching assumptions, thereby offering a more reliable method for predicting barricade loading in mine environments.
This study utilizes finite element analysis on Tehran Metro Line 6 to demonstrate that increasing train speeds, particularly when two trains operate simultaneously at 90 km/h, significantly intensifies ground surface vibrations to levels (84 dB) that may disturb nearby residents.
This paper introduces FBG-PhysNet, a physics-informed recurrent neural network that solves the underdetermined inverse problem of separating temperature and strain from single-fibre Bragg grating measurements in lithium-ion batteries, enabling real-time internal thermo-mechanical observability with high accuracy and low cross-talk.
This study pioneers an integrated workflow combining 3D scanning, finite element simulation, and intelligent optimization to compare the acoustic characteristics of historical Qing Sunaiyi and modern Suona, ultimately quantifying the impact of bell geometry on radiation performance and optimizing the modern instrument's design for enhanced sound pressure levels.
This study proposes a physics-informed Gaussian Process Regression framework that accurately predicts the pressure drop decay of porous fiber matrices and quantifies uncertainty to identify an optimal 2-to-61.1-hour production window, thereby enabling intelligent, data-driven scheduling and quality control in advanced manufacturing.