Journal of Science and Transport Technology

Thermally induced nonlinear instability of functionally graded plates including pores and elastic boundary restraint

2025-12-04T16:59:38+00:00

This study peruses the thermally caused nonlinear instability of flat panels made up functionally graded material (FGM) with porosity and elastic edge restraint. The pores are introduced in FGM via uniform and nonuniform patterns. The temperature-dependent (T-D) characteristics of constitutive materials are captured and overall characteristics of imperfect FGM are evaluated adopting a modification of mix rule. Fundamental derivations of large-deflection instability issue are derived employing first order shear deformation theory (FSDT) incorporating imperfect geometry and interaction from two-parameter foundation. The resolutions of transverse displacement, stress function (SF) along with rotations are used to fulfil simple support conditions on boundaries and Galerkin procedure combined with an iterative process are employed to obtain critical temperatures and post-critical paths. Parametric studies explore that porosity beneficially influences the buckling withstanding and post-critical strength of thermally loaded FGM plates. In contrast, tangential constraints of edges have deteriorative effects of nonlinear instability of porous FGM plates undergoing thermal loadings.

Assessing the Determinants of Maintenance Effectiveness in Aged Buildings Using Structural Equation Modelling: An Empirical Study in Vietnam

2025-12-16T02:47:08+00:00

Effective management of aging buildings presents significant economic and technical challenges, particularly in large urban centers such as Ho Chi Minh City, Vietnam, where numerous high-rise structures were constructed prior to 1975. Because operation and maintenance can comprise nearly 80% of overall life-cycle costs, pinpointing the factors that impede maintenance effectiveness is of vital importance. The present study constructs and validates a model to examine and quantify the critical factors that hinder Building Maintenance Effectiveness (BME). The study was carried out using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data obtained from 147 valid responses of maintenance experts in Ho Chi Minh City, Vietnam. The suggested model exhibits substantial explanatory capacity, clarifying 74.5% of the variance in BME (R² = 0.745). The analysis reveals five statistically significant negative determinants: Building Management Factors (BMF), Budget and Financial-Related Factors (BFRF), Technical-Related Factors (TRF), Technology Application-Related Factors (TARF), and Building User-Related Factors (BURF). Among these, deficiencies in technology application (TARF) emerged as the most critical barrier (f² = 0.351), followed by moderate effects from management (BMF) and financial (BFRF) constraints. The study provides a data-driven analytical framework to support facility managers and policymakers in prioritizing investment resources, while emphasizing technology adoption as the most strategic intervention for optimizing performance and ensuring sustainable development of building assets.

Interpretable Machine Learning Model for Evaluating Flexural Strength of Ultra High-Performance Concrete

2025-12-14T18:47:21+00:00

Ultra-high-performance concrete (UHPC) mix design remains experimentally expensive because many ingredients interact nonlinearly to govern flexural behavior. An interpretable machine-learning pipeline was developed to predict UHPC flexural strength from literature-derived mixes (317 observations, 14 input variables). Nine regression models were screened under a rigorous Monte-Carlo protocol (1,000 random 70/30 splits). Tree-based boosting dominated: on a representative split, CatBoost achieved R²test=0.928, RMSE = 1.980 MPa, MAE = 1.454 MPa, MAPE = 8.386%, with XGBoost close behind; Random Forest and Gradient Boosting formed a reliable second tier, while linear, SVR, and KNN underfit. Global and local interpretability (SHAP and PDP) revealed a stable hierarchy of drivers: steel fiber content and curing time were strongly beneficial; coarse aggregate content was deleterious and nearly monotonic; water became increasingly harmful at high dosages; superplasticizer exhibited an interior “sweet spot”; cement and silica fume were favorable (silica fume above ~100 kg/m³); sand was weakly positive; limestone powder was near-neutral. Guided by mean|SHAP|, the feature set was reduced from 14 to 9 variables with only a modest trade-off (R²test =0.916, RMSE = 2.143 MPa, MAE = 1.522 MPa, MAPE = 9.07%). External verification on an independent dataset confirmed generalization and preserved the correct nonlinear response to steel fibers in the practical 0-2% range. A lightweight GUI operationalizes the nine-input model, enabling rapid “what-if” exploration and reducing measurement burden by 36%. The results deliver both accuracy and transparency, distilling actionable rules for UHPC tailored to flexure-critical applications: prioritize steel fibers and adequate curing, cap coarse aggregate, and maintain water/superplasticizer within stable windows while using cement and silica fume to tune the matrix.

Nonlinear dynamical response of FG sandwich plates with pores and flexibly constrained boundaries in thermal environments

2025-12-10T18:58:51+00:00

The present article scrutinizes the nonlinear transient response (NTR) of functionally graded material (FGM) sandwich plates including the simultaneous impacts of pores, geometric perturbation, high temperature, and flexible constraints of boundaries. The characteristics of constitutive materials are depended on temperature and overall features of imperfect FGM are sought employing a modification of linear rule of mix. Two sandwich configurations fabricated from FGM and homogeneous layers are considered, and pores are evenly distributed in materials. Fundamental derivations via transverse displacement along with stress function (SF) are built on the foundation of the first order plate theory (FOPT) incorporating initial geometric imperfection and von Kármán terms. The derived equations are resolved by adopting analytic derivations combined with Galerkin approach to yield a differential equation with nonlinear terms. The derived differential equation is resolved by virtue of taking up the Runge–Kutta integration diagram to graph the temporal transverse displacement (TD)–time curves of transient response of sandwich plates. An illustrative analysis is implemented to evaluate diverse impacts of pore volume ratio, imperfection, tangential restraints of boundaries, and high temperature on the nonlinear transient response. It is explored that in-plane edge confinements substantially influence the nonlinear dynamic response, especially at high temperatures. Furthermore, the thickness of skins and size of geometrical imperfection significantly affect temporal TD–time paths.

Nonlinear dynamic responses of functionally graded graphene platelet reinforced composite spherical caps subjected to impulse loads

2025-12-10T19:03:48+00:00

This paper investigates the nonlinear dynamic behavior of functionally graded graphene platelet reinforced composite (FG-GPLRC) shallow axisymmetric spherical caps resting on a nonlinear viscoelastic Pasternak foundation and subjected to transient mechanical loading. The caps are exposed to a thermal environment, yet the mechanical properties of the material are considered temperature-independent. The theoretical framework is established based on higher-order shear deformation theory (HSDT) integrated with von Karman geometric nonlinearity. The governing equations of motion are derived using the Lagrangian approach, incorporating damping effects through the Rayleigh dissipation function. A semi-analytical solution is obtained by combining the Ritz method for spatial discretization and the Runge-Kutta method for time integration. Two forms of impulsive pressure, infinite duration step load and blast load, are examined to evaluate transient responses. Parametric studies are conducted to explore the effects of graphene distribution patterns, mass fraction, geometric parameters, thermal pre-deflection, foundation stiffness, and damping coefficients on the nonlinear response.

Building a miniature experimental model for camouflage underwater explosions

2025-12-23T03:28:20+00:00

In general, explosion experiments on miniature models are research methods commonly used in explosion research. Although the method of experimental explosion research on miniature models in laboratory conditions is very meaningful for practical application but up to now, there have been no published papers on the method of calculating and designing parameters on the size of miniature models that satisfy the conditions of camouflage underwater explosions (UNDEX), so that similar explosive effect laws when carrying out UNDEX in rivers and seas can be drawn, whose factors converting research results from miniature models to reality are also obtained. For the above reason, the paper aims to analyze the theoretical basis of the similarity model of an explosion, proposing a method for calculating and verifying the parameters of miniature experimental models on underwater explosion effects, conducting experiments, and establishing experimental laws. The result is a miniature model for experimental research on underwater explosions that satisfies the conditions of blasting in infinite environments and satisfies the similarity principle of shock wave pressure in water environments with a model correction factor of 1.45 to 1.47 times when compared to the methods of Russia and the United States, respectively.

AI-Integrated BIM Education: A Conceptual Framework for Process Competencies Aligned with Industry Workforce Demands

2025-12-18T02:57:04+00:00

Building Information Modelling (BIM) has evolved from a visualisation aid to a process-driven methodology that demands interdisciplinary collaboration and rigorous information management aligned with ISO 19650. Yet many curricula still prioritise software proficiency over process understanding, leaving graduates under-prepared for BIM coordination, information management and decision-making. At the same time, rapid adoption of artificial intelligence (AI) in construction is reshaping BIM workflows and amplifying existing skills gaps. This conceptual paper develops a theoretically grounded framework for integrating AI into BIM education to cultivate both technical and process-oriented competencies. Drawing on the Technology Acceptance Model, constructivist learning theory and cognitive load theory, the framework positions AI as a cognitive scaffold that shifts students’ effort from routine modelling operations towards higher-order process reasoning. It specifies a scaffolded progression of AI use, authentic ISO 19650-aligned project work, collaborative interdisciplinary learning structures and assessment strategies that foreground process competencies rather than isolated software skills. The framework’s distinctive contribution lies in its explicit integration of three theoretical lenses, systematic mapping of learning outcomes to ISO 19650 and buildingSMART certification domains, and operational guidance through worked examples of AI-integrated instruction. Although conceptual and awaiting empirical validation, the framework offers actionable guidance for programme leaders and educators designing AI-enabled BIM curricula. It contributes to educational technology scholarship by illustrating how established learning theories can structure AI integration in technical education and by proposing AI as a pedagogical tool for addressing critical workforce development challenges in the construction industry.

Semi-analytical approach for nonlinear dynamic responses of complex curved functionally graded graphene metal matrix reinforced panels subjected to impulse loads

2025-12-10T19:09:44+00:00

This study develops a semi-analytical framework to investigate the nonlinear dynamic behavior of functionally graded graphene-reinforced metal matrix composite (FG-GRMMC) shallow panels with complex curvatures under thermal environments. Three panel geometries, cylindrical, parabolic, and sinusoid, are examined under impulsive loading conditions, including finite duration step and triangular blast loads. The analysis is formulated within the higher-order shear deformation theory (HSDT), incorporating von Kármán geometric nonlinearities. To address the geometric complexity, the stress function is approximated using an average-based like-Galerkin technique that satisfies the compatibility condition. The nonlinear governing equations of motion are derived via the Lagrange principle, with structural damping modeled through the Rayleigh dissipation function. Numerical simulations employing the fourth-order Runge-Kutta method are conducted to capture the time-dependent deflection responses. The results provide insights into the influence of panel geometry, graphene distribution, and impulse load characteristics on the nonlinear dynamic performance of FG-GRMMC panels.

GIS-Based Flow-R Model for Debris Flow Susceptibility Mapping: A Case Study from Muong Bo, Lao Cai, Vietnam

2025-12-16T01:28:42+00:00

Landslides and debris flows are frequent hazards in Vietnam’s mountainous regions, causing severe socio-economic damage that has intensified under climate change and extreme rainfall conditions. This study applies the open-source GIS-based Flow-R model to assess debris-flow susceptibility and runout characteristics in Muong Bo Commune, Lao Cai Province, northwestern Vietnam (specifically focusing on the highly vulnerable area around the Nam Cang stream). The methodology involved developing a geospatial database and simulating potential initiation and propagation zones. A major debris-flow event that occurred on 12 September 2023 was used for model calibration and validation. Field validation confirmed the model’s robust capability to simulate flow runout distances based on observed data. Seventy percent of the mapped debris-flow initiation points were used for calibration, and 30% for validation[/RED], and model performance was evaluated using the Area Under the ROC Curve (AUC). The Flow-R model achieved an AUC value of 0.868, indicating good predictive capability[/RED], while 48.5% of observed debris-flow initiation points were correctly predicted. Results demonstrate that Flow-R effectively delineates high-susceptibility source zones and plausible debris-flow runout paths, particularly for medium- to large-scale events. The novelty of this study lies in the first integrated application of Flow-R, combined with systematic field validation in northwestern Vietnam, and the coupling of detailed geological–geomorphological characterization with susceptibility and runout modeling, providing a transferable framework for debris-flow assessment in data-scarce mountainous regions.

RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis - A Case Study for the Semiconductor Sector

2025-12-18T04:14:58+00:00

Financial analysis relies heavily on the interpretation of earnings reports to assess company performance and guide decision-making. Traditional methods for generating such analyses require significant financial expertise and are often time-consuming. With the rapid advancement of Large Language Models (LLMs), domain-specific adaptations have emerged for financial tasks such as sentiment analysis and entity recognition. This paper introduces RAG-IT (Retrieval-Augmented Instruction Tuning), a novel framework designed to automate the generation of earnings report analysis through an LLM fine-tuned specifically for the financial domain. Our approach integrates retrieval augmentation with instruction-based fine-tuning to enhance factual accuracy, contextual relevance, and domain adaptability. We construct a sector-specific financial instruction dataset derived from semiconductor industry documents to guide the LLM adaptation to specialized financial reasoning. Using NVIDIA, AMD, and Broadcom as representative companies, our case study demonstrates that RAG-IT substantially improves a generalpurpose open-source LLM and achieves performance comparable to commercial systems like GPT-3.5 on financial report generation tasks. This research highlights the potential of retrieval-augmented instruction tuning to streamline and elevate financial analysis automation, advancing the broader field of intelligent financial reporting.

Vehicle Classification Using Combined Laser Rangefinder and Pyroelectric Infrared Sensors in a Real Dynamic Environment

2025-12-11T08:34:22+00:00

This paper presents a vehicle classification approach for real-world dynamic environments based on sensor fusion between a laser rangefinder (LRF) and a pyroelectric infrared (PIR) sensor. By integrating geometric shape information from the LRF with thermal distribution patterns captured by the PIR sensor, the system extracts distinctive features that effectively suppress noise introduced by external environmental variations. A lightweight neural network is developed for classification, achieving a minimum accuracy of 91% for specific vehicle types and an average accuracy of 94% across all categories. Owing to its high accuracy and low computational cost, the proposed model is well-suited for implementation in portable embedded platforms, functioning as intelligent measurement nodes within Intelligent Transportation Systems (ITS).

Cementitious material based on portland cement and ground granulated blast furnace slag for ground improvement of construction sites in the Mekong Delta

2025-11-19T08:42:45+00:00

This paper presents the results of a study on the selection of binder compositions based on Portland cement combined with ground granulated blast furnace slag (GGBFS) and gypsum for ground improvement using the deep mixing method (cement deep mixing – CDM) and shallow stabilization for road base and foundation works. The study focuses on weak and aggressive foundation soils in the Mekong Delta region. The binder compositions were determined through experimental investigations using three representative types of foundation soils (clayey sand, sandy clay, and high-plasticity clay) collected from expressway construction projects in the Mekong Delta region. Unconfined compressive strength (UCS) was evaluated at curing ages of 3, 7, 28, and 91 days, while durability performance was assessed through immersion in natural seawater up to 6 months. The results show that, relative to soils stabilized with ordinary Portland cement (PC40), the selected binder system—comprising PC40 combined with 55–65% GGBFS and 3–5% gypsum—resulted in a 28-day UCS increase of approximately 1.6 to 2.3 times. The stabilized soils with this binder also exhibited significantly improved durability when immersed in seawater. No cracking or failure was observed after 6 months of immersion, whereas the samples stabilized with ordinary Portland cement showed initial cracking after 3 months and complete failure after 6 months.

Thermo-Mechanical Post-Buckling Analysis of 3D Graphene Foam Shallow Spherical Shells and Circular Plates Stiffened by a Spider-Web Stiffener System: A Stress Function Approach

2025-12-10T19:22:35+00:00

This study focuses on analyzing the nonlinear postbuckling response of shallow spherical shells and circular plates resting on a nonlinear elastic foundation and subjected to combined mechanical and thermal loads. The spherical shell or circular plate is made of 3D graphene foam material and is stiffened by a spider-web stiffener system, which is also composed of 3D graphene foam. The fundamental formulations and governing equations are derived based on Donnell’s shell theory, incorporating von Kármán geometric nonlinearity. The stiffness components of the stiffened plate/shell structures are determined using the extended stiffener-smearing technique. By employing the stress function approach and the Ritz energy method, analytical expressions for the load-deflection relationship are obtained. Numerical examples are then carried out to investigate the postbuckling behavior of the stiffened shallow spherical shells and circular plates under the influence of material, geometric, and foundation parameters.

Nonlinear buckling and postbuckling responses of sandwich cylindrical panels with layered corrugated FG-GRC core resting on elastic foundation

2025-12-10T19:26:31+00:00

An analytical framework is developed to investigate the nonlinear buckling and postbuckling behavior of sandwich cylindrical panels composed of functionally graded graphene-reinforced composite (FG-GRC) face sheets and a layered corrugated FG-GRC core. The panels are subjected to axial compression and external pressure while resting on Pasternak elastic foundations in a thermal environment. A modified homogenization model is proposed for the layered FG-GRC corrugated core, extending previous formulations of single-layer configurations to include thermal effects. Two types of corrugation geometries, trapezoidal and round, are considered, and various graphene distribution patterns are applied to optimize the stiffness of the FG-GRC core. The governing equations are derived using Donnell-type shell theory in conjunction with von Karman geometric nonlinearity, and the Ritz energy method is employed to obtain the critical buckling load expression and postbuckling equilibrium paths. Parametric studies are conducted to assess the influence of core geometry, graphene distribution, elastic foundation stiffness, thermal effects, and panel dimensions. The results highlight the substantial improvements in structural stability offered by layered corrugated cores and provide valuable insights for the design of advanced FG-GRC sandwich structures under combined mechanical and thermal loads.

An analytical approach for nonlinear thermal buckling and postbuckling behavior of functionally graded graphene platelet-reinforced composite conical shells

2025-12-10T19:43:01+00:00

This study presents an analytical investigation into the nonlinear thermal buckling behavior of functionally graded graphene platelet-reinforced composite (FG-GPLRC) conical shells resting on a nonlinear elastic foundation. The formulation is developed based on Donnell shell theory in conjunction with von Kármán geometric nonlinearity. The nonlinear foundation is characterized by three stiffness parameters that capture both hardening and softening behaviors, corresponding to positive and negative nonlinear parameters, respectively. Employing the Ritz energy method, thermal load–deflection relationships are derived to analyze both the critical buckling temperature and the postbuckling response of the structure. The influence of key parameters, including the stiffness of the elastic medium, graphene platelet (GPL) mass fraction, material gradation profiles, and geometric configurations, on the nonlinear thermal buckling performance is thoroughly examined through numerical simulations.

Nonlinear thermo-mechanical buckling responses of FG-GPLRC catenary caps and circular plates

2025-12-10T19:48:39+00:00

An analytical framework is developed to investigate the nonlinear thermal and mechanical buckling behavior of functionally graded graphene platelet-reinforced composite (FG-GPLRC) catenary caps resting on nonlinear elastic foundations. The formulation is based on the first-order shear deformation theory (FSDT) coupled with von Kármán-type geometric nonlinearity. The cap geometry follows a catenary profile, from which spherical caps and circular plates emerge as special cases when the curvature radius is constant or tends to infinity, respectively. The graphene platelets are embedded within a copper matrix and graded through the thickness using various distribution patterns. By employing the Galerkin method, explicit expressions for thermal critical loads and postbuckling paths under both thermal and external pressure are derived. A comprehensive parametric study is performed to examine the influences of cap geometry, foundation parameters, and GPL distribution on the stability characteristics, offering insights into the design of curved FG-GPLRC structures under combined loading conditions.

Analytical study on nonlinear buckling of spiral-stiffened FG-CNTRC toroidal shells segments with piezoelectric layers and generalized curvature under external pressure

2025-12-11T08:50:06+00:00

This paper presents an analytical investigation into the nonlinear stability of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) toroidal shell segments with piezoelectric layers and generalized curvature subjected to external pressure. The analysis incorporates several typical meridian shapes, including circular, parabolic, and half-sinusoid geometries. The paper uses Donnell shell theory (DST), combined with von Kármán geometric nonlinearity and an improved Lekhnitskii smeared stiffener technique to study the behavior of stiffened spiral or orthogonal FG-CNTRC shells. The carbon nanotube (CNT) distributions in the shells and stiffeners are designed to satisfy material continuity conditions. Thermal effects and elastic foundation interactions are also incorporated into the analysis, providing a comprehensive framework for understanding their influence on structural stability. To examine large deformation behavior, three-term deflection functions fulfilling the simply supported boundary conditions are adopted, whereas the Ritz energy approach is utilized to derive the load-deflection relationship under external pressure. The numerical results indicate that the critical buckling loads and postbuckling responses are significantly affected by the spiral stiffener system, elastic foundation, temperature variations, and CNT distributions.

Cross-Branch CNN-MLP Integration for Improving Landslide Spatial Probability on Mt. Umyeon, Korea

2025-11-06T03:37:29+00:00

Accurate maps of landslide spatial probability (LSP) are crucial for planning and risk reduction in steep, urbanized areas. A hybrid Convolutional Neural Network (CNN) - Multilayer Perceptron (MLP) model is introduced for mapping LSP in Mt. Umyeon, Korea. The design combines two complementary views of each location: a convolutional branch learns spatial context from multi-channel image patches. At the same time, a multilayer perceptron captures local numeric and categorical attributes at the central point. Feature-level fusion concatenates the embeddings from both branches and feeds a lightweight classifier to produce probabilities. Performance was assessed under consistent data splits and training protocols. Training AUCs reached 0.887 (MLP), 0.894 (CNN), and 0.903 (CNN-MLP). More importantly, validation AUCs were 0.808 (MLP), 0.821 (CNN), and 0.854 (CNN-MLP), indicating stronger generalization for the fused representation. These gains reflect the complementary nature of neighborhood structure learned by the CNN and pointwise information stabilized by the MLP. The results show that a compact, feature-level fusion of CNN and MLP can materially improve spatial probability mapping of landslides. The approach provides a practical route to more reliable probability surfaces for decision support in mountainous urban regions.

Experimental evaluation of modulus of elasticity of concrete using limestone and basalt aggregates determined according to different standard test methods

2025-12-18T03:40:15+00:00

The modulus of elasticity of concrete is influenced not only by its compressive strength but also by the geological origin of the coarse aggregate used. However, current standards employ diverse testing methods and predictive equations, many of which neglect the aggregate’s contribution. This study presents experimental data for concrete with compressive strengths ranging from 30 MPa to 55 MPa, produced using two Vietnamese coarse aggregates: Ha Nam limestone and Hoa Binh basalt. The modulus of elasticity was measured according to three standards: EN 12390-13:2021, ASTM C469-10, and TCVN 5726-2022. At a constant water-to-cement ratio, concrete incorporating Ha Nam limestone exhibited comparable compressive strength to that of Hoa Binh basalt, yet demonstrated a higher elastic modulus. Based on the experimental data, this study proposes using preliminary coefficients of 21,000 and 19,700 - corresponding to Ha Nam limestone and Hoa Binh basalt aggregates, respectively - for estimating the modulus of elasticity in accordance with EN 1992-1-1:2004. For estimations in accordance with ACI CODE-318-25, the recommended preliminary factors are 5,250 and 4,810, respectively.

Landslide detection and susceptibility analysis: A case study in Pieng stream catchment, Son La province

2025-12-18T02:36:04+00:00

Landslides are a major natural hazard causing significant property damage and loss of life worldwide. In this study, an enhanced landslide inventory was developed for the Pieng Stream catchment (Son La Province) using Object-Based Image Analysis (OBIA) combined with field surveys. Twelve conditioning factors were used to model landslide susceptibility through four machine learning algorithms: extreme gradient boosting (XGB), random forest (RF), multi-layer perceptron (MLP) and logistic regression (LR). Additionally, model interpretation was supported by SHAP, MDA, and PDP analyses. The results demonstrated a high level of reliability for the OBIA method (TPR = 0.886, TS = 0.602). Among the tested models, the XGB model showed the best performance, achieving an AUC of 0.961, an F1 score of 0.915, and an accuracy of 0.915 on the testing dataset. The two most influential predictors identified were lineament density and aspect. An increase in landslide probability was observed with increasing slope, relative relief, lineament density, river density and aspect (0-150°). A total of 88% of testing landslide points were correctly classified within high to very high susceptibility areas, while areas outside the AOA covered merely 0.79% of the study region, indicating a high level of model applicability.