Journal of Science and Transport Technology

Enhancing landslide susceptibility prediction using deep learning through optimal activation and loss function selection

Tran Huu Anh, Tran Van Phong, Dung Nguyen Duy, Nguyen Thi Bich Hanh — Mon, 05 Oct 2026 00:00:00 +0000

Landslide susceptibility prediction has increasingly shifted from traditional statistical methods toward deep learning approaches capable of modeling complex nonlinear relationships among geo-environmental controlling factors. However, model performance is influenced not only by network architecture but also by the selection of activation and loss functions, which directly affect learning dynamics and generalization capability. This study systematically investigates the influence of activation–loss function combinations within a unified deep learning framework for landslide susceptibility mapping in Sin Ho area, northwestern Vietnam. A multi-source geospatial database integrating topographic, geological, hydrological, land-use, seismic, and rainfall-related factors was used to train and validate multiple model configurations. Model performance was evaluated using a comprehensive set of quantitative indicators, including area under the ROC curve (AUC) and accuracy (ACC). The results indicate that models employing the Softmax activation function consistently achieve superior predictive performance and robustness. Among all tested configurations, the Softmax–MultiLabel (SM–ML) model delivered the most reliable results, attaining a validation AUC of 0.878, and ACC of 83.75, demonstrating strong discrimination ability, stable generalization, and reduced prediction error. The landslide susceptibility map generated using the optimal SM–ML configuration exhibits a clear and progressive increase in landslide density from very low to very high susceptibility classes, confirming meaningful spatial stratification of landslide hazard. These findings highlight that careful selection of activation and loss functions can substantially enhance deep learning performance without increasing model complexity or computational cost, providing a robust and efficient framework for landslide susceptibility assessment and disaster-risk reduction in mountainous regions under changing climatic conditions.

Foam-based eco-lightweight controlled low-strength material for high-volume utilization of municipal solid waste incineration bottom ash

Trong-Phuoc Huynh, Khoa Dang Truong, Lai Bao Tan, Tri Ho Minh Le — Thu, 15 Oct 2026 00:00:00 +0000

The increasing scarcity of natural aggregates, coupled with the growing environmental burden associated with municipal solid waste incineration (MSWI) residues, poses a dual challenge for sustainable urban construction. Previous studies have primarily investigated MSWI bottom ash (IBA) or foam-modified mortars in isolation; however, the combined effects of IBA, fly ash, and pre-formed foam on controlled low-strength material (CLSM) have not yet been systematically evaluated. This study develops an integrated, waste-valorized CLSM system and systematically investigates the fresh-state, hardened, durability, and eco-mechanical performance of fifteen mixtures incorporating 0–40% IBA, 0–20% fly ash, and 0–20% pre-formed foam. Cylindrical specimens were tested in accordance with relevant ASTM standards to evaluate flowability, bleeding, setting time, water absorption, 3–28-day compressive strength, and durability under simulated acid rain and seawater immersion, supported by a normalized multi-criteria assessment (MCA) and eco-mechanical indicators (relative carbon emission [RCE] and relative energy consumption [REC]). The results indicate that increasing IBA content reduced flowability from 170 mm to 130 mm and increased water absorption by up to 49%, whereas foam incorporation significantly enhanced flowability to a maximum of 270 mm, albeit with an associated increase in water absorption to 5.44–5.91%. The 28-day compressive strength decreased from 3.213 MPa in the control mixture to 2.187 MPa at the highest combined waste and foam content; nevertheless, all mixtures exceeded the minimum 1 MPa requirement for CLSM applications. Durability losses ranged from 4.77% to 10.51% under acid rain exposure and from 8.34% to 16.61% under seawater immersion, with high-porosity mixtures being the most adversely affected. Despite these trade-offs, mixtures with moderate waste contents—particularly M5 (10% IBA, 10% fly ash, and 10% foam)—exhibited a well-balanced performance, achieving an MCA score of 0.66, a 28-day compressive strength exceeding 2.5 MPa, a flowability of approximately 210 mm, and 85–88% strength retention under aggressive environments. Overall, the findings indicate that the combined use of IBA, fly ash, and pre-formed foam can produce lightweight CLSM with a favorable balance between engineering performance and strength-normalized environmental efficiency, making it promising for non-structural applications such as coastal and municipal backfilling.

An efficient hybrid framework for reliability-based size and shape optimization of trusses considering uncertainties

Anh Hoang Nguyen, Qui Xuan Lieu, Van Quan Tran, Quan Minh Lieu — Sun, 04 Oct 2026 00:00:00 +0000

Reliability-based design optimization (RBDO) of truss structures has become a powerful tool for achieving optimal designs under uncertainties in material properties, cross-sectional areas, and applied loadings. By integrating the sequential optimization and reliability assessment (SORA) into an efficient optimization tool based on the success-history-based adaptive differential evolution (SHADE) algorithm, this paper presents a novel RBDO methodology, which is the so-called SORA-SHADE, for the optimization of shape and size in truss structures under uncertainties of input data. The proposed paradigm is tested on well-known 2D and 3D trusses with and without the reliability analyses for validation. For reliability-based analysis cases, each cross-sectional area is considered as a random variable under uncertainties. The results show that the proposed approach is highly effective and reliable in finding optimal designs that meet high reliability requirements.

An ensemble JCHAIDStar framework for improved landslide susceptibility prediction using Bagging and Random Subspace techniques

Vu Cao Dat, Tran Hong Thai, Pham Thi Thanh Nga, Pham Quang Dung — Thu, 15 Oct 2026 00:00:00 +0000

This study developed a landslide susceptibility map for the mountainous area of northern Vietnam, located within the administrative boundary of the former Ha Giang Province and now under Tuyen Quang Province, to support the identification of landslide-prone areas and disaster risk reduction. The input data included a landslide inventory of 963 locations and 13 conditioning factors representing topographic, hydrological, geological, climatic, and human-related conditions. The dataset was divided into training and validation subsets using a 70:30 ratio. Four models, including Bagging-based JCHAIDStar (Bag-JCHAIDStar), Random Subspace-based JCHAIDStar (RSS-JCHAIDStar), Deep Neural Network (DNN), and Multilayer Perceptron (MLP), were developed and compared using model performance metrics and Taylor diagram analysis. The results show that Bag-JCHAIDStar achieved the best overall performance, with AUC = 0.988 for the training dataset and AUC = 0.950 for the validation dataset. On the validation dataset, this model also obtained ACC = 88.7%, Kappa = 0.760, and RMSE = 0.291, indicating good classification and generalization capability. The validation of the landslide susceptibility zoning map using the Frequency Ratio method showed that the very high susceptibility class had the highest landslide proportion, accounting for 52.11% of the total landslide pixels and reaching the highest FR value of 1.817. These results suggest that the Bag-JCHAIDStar model can provide useful support for landslide susceptibility assessment, disaster risk reduction, and sustainable land-use planning in mountainous areas.

A hybrid linear regression and CatBoost model for high-performance nonlinear static analysis of functionally graded plates

Do Thi Thanh Dieu, Son Thai — Fri, 02 Oct 2026 00:00:00 +0000

Nonlinear static analysis of functionally graded material (FGM) plates using high-fidelity numerical methods such as Isogeometric Analysis (IGA) is computationally demanding because it requires iterative solution procedures to trace the equilibrium path. Recently, deep learning surrogates (e.g., Long Short-Term Memory, LSTM) have been explored to reduce repeated simulations; however, they can still require nontrivial training effort and careful model tuning. This study proposes a high-performance hybrid surrogate model combining Linear Regression (LR) and CatBoost to predict the nonlinear load-deflection behavior of FGM plates. The equilibrium-path response is formulated as a sequence regression task along pseudo-time load steps and decomposed into two stages: LR captures the global linear trend, while CatBoost learns the nonlinear residual component not represented by LR. The proposed approach is trained and validated using IGA-generated datasets. It is evaluated under different data-availability settings (30%, 50%, and 80% of the equilibrium-path sequence used for training and testing) and assessed in terms of prediction accuracy, hyperparameter sensitivity, and end-to-end computational cost (training + prediction). In our implementation, the combined LR-CatBoost training and prediction time is approximately 0.25 s for the baseline case, excluding the IGA computation required to generate the initially available response segment. Prediction accuracy is high, with mean absolute percentage errors on the order of 10^-2 to 10^-1% depending on the case. These findings indicate that the proposed LR-CatBoost strategy provides a robust and ultra-fast surrogate for near-real-time nonlinear structural analysis of FGM plates.

Experimental study on the performance of a slotted wells turbine in an oscillating water column device

Thuc Tri Dang, Huy Hoang Truong, Vay Siu Lo, Thien Tich Truong — Wed, 14 Oct 2026 00:00:00 +0000

Ocean wave energy has garnered significant attention as a sustainable power source, with the Oscillating Water Column (OWC) device emerging as a highly effective technology for wave-to-electricity conversion. Within this system, the Wells turbine serves as a critical component for energy extraction. While current literature predominantly relies on Computational Fluid Dynamics (CFD) simulations to analyze the performance of these turbines, this study employs a rigorous experimental approach to evaluate their dynamics. The experimental results demonstrate high reliability when compared with CFD simulation data. Furthermore, this research evaluates the performance of a slotted Wells turbine design. The comparative analysis reveals that the slotted Wells turbine achieves superior efficiency compared to the conventional Wells turbine design. These findings offer a comprehensive empirical foundation and provide critical insights for the future optimization of wave energy converters. Ultimately, this study serves as an essential precursor to subsequent developments aimed at enhancing the operational viability of OWC systems in practical, real-world applications.