Saved Queries

Accurate freeway Improvements in traffic state prediction accuracy and enhanced stability enable more proactive traffic control and demand management strategies, thereby reducing congestion spillover effects, unnecessary acceleration–deceleration cycles, and the resulting fuel consumption and emissions. Yet, this remains challenging due to the interplay between deterministic traffic flow mechanisms and stochastic disturbances. Purely data-driven models suffer from error accumulation under out-of-distribution conditions, while physics-based models lack flexibility in capturing nonlinear deviations. This paper proposes MDURP, a physics-constrained residual learning framework that reformulates prediction as a residual-space learning problem. A calibrated Cell Transmission Model generates a physically admissible baseline; deep learning models are then restricted to learning the residuals. Wavelet decomposition and GARCH volatility modeling address the multi-scale and heteroskedastic characteristics of these residuals. Experimental results demonstrate that MDURP consistently outperforms baseline models, reducing MAE by an average of 6.8%, RMSE by an average of 4%. The framework also suppresses long-term error accumulation, with MAPE escalation slowing from 0.79% to 0.58% per step. These gains confirm that anchoring deep learning within a physics-defined residual space enhances both accuracy and stability. Full article

(This article belongs to the Section Sustainable Transportation)

►▼ Show Figures

Figure 1

16 pages, 1572 KB

Open AccessArticle

Task-Aware Decoupled State-Space Model for Multi-Task Satellite Internet Evaluation

by Erlong Wei, Peixuan (Nolan) Kang, Yihong Wen and Kejian Song

Electronics 2026, 15(7), 1369; https://doi.org/10.3390/electronics15071369 (registering DOI) - 25 Mar 2026

Abstract

Multi-task learning (MTL) is essential for satellite internet systems requiring simultaneous optimization of beam management, interference mitigation, resource allocation, and traffic prediction. However, existing evaluation methods rely predominantly on external performance metrics, neglecting internal dynamics governing task interactions. We propose TDS-Mamba (Task-Aware Decoupled State-Space Model), integrating selective state-space models with task-specific modulation for satellite networks. Our contributions include: (1) Task-Aware Decoupled S6 (TA-DS6) with hypernetwork-generated task-conditioned projection matrices; (2) Shared–Private State Decomposition disentangling cross-task representations from task-specific features; (3) Value-at-Risk (VaR) Gating for risk-sensitive optimization under varying orbital conditions; and (4) an internal diagnostic framework with Task-Specific Entropy and Interference Coefficient metrics. Experiments on LEO satellite constellation benchmarks show consistent improvements over the selected baselines and provide enhanced interpretability of multi-task dynamics via internal diagnostics. Full article

►▼ Show Figures

Figure 1

25 pages, 2749 KB

Open AccessArticle

GPCN: A Decomposition-Based Hybrid Model for a Lithium-Ion Capacity Forecasting and RUL Inference Framework

by Li Wang, Guosheng Cai, Yuan Gao and Caoxin Shen

World Electr. Veh. J. 2026, 17(4), 171; https://doi.org/10.3390/wevj17040171 (registering DOI) - 25 Mar 2026

Abstract

To address the non-stationary fluctuations caused by capacity regeneration and measurement noise during lithium-ion battery aging, this paper proposes a decomposition-guided heterogeneous prognostic framework for capacity forecasting and remaining useful life (RUL) inference. First, the raw capacity sequence is decomposed by CEEMDAN to separate the long-term degradation trend from short-term regeneration-related disturbances across different time scales. Next, a temporal convolutional network (TCN) is employed to model the trend component, while Gaussian process regression (GPR) is used to characterize local fluctuation behavior and provide predictive uncertainty. Finally, Dempster–Shafer (D-S) evidence theory is introduced to fuse multi-source prognostic outputs, yielding a more robust capacity trajectory for end-of-life (EOL) threshold localization and RUL estimation. Experiments are conducted on the lithium-ion battery dataset released by NASA Ames. Across the four tested battery cells, the proposed method achieves RMSE values of 0.0257–0.0445 Ah and EOL cycle deviations of 1.17–5.53 cycles, while yielding a more balanced trade-off than representative baselines between point-wise prediction accuracy and threshold-crossing stability. Moreover, under direct multi-step forecasting, the prediction error increases with the forecasting horizon, which is consistent with the expected characteristics of long-horizon capacity extrapolation. Overall, this work provides an implementable and interpretable prognostic framework for battery health assessment in the presence of capacity regeneration phenomena. Full article

(This article belongs to the Section Storage Systems)

23 pages, 3657 KB

Open AccessArticle

Performance of the Intumescent Coatings in Structural Fire via ANN-Based Predictive Models

by Kin Ip Chu and Majid Aleyaasin

Fire 2026, 9(4), 142; https://doi.org/10.3390/fire9040142 (registering DOI) - 25 Mar 2026

Abstract

In this paper, an Artificial Neural Network (ANN) is built to predict the performance of intumescent coatings subjected to the ISO 384 fire curve. The performance metric is called the Retention Loss Onset Time (RLOT) in the structural steel. The network receives the steel and coating thicknesses as input and provides RLOT as the performance of any intumescent coating in a fire accident with substantial accuracy. The required data for obtaining the model is provided by revisiting the recent attempts in this field, which include hybrid numerical and experimental methods. It is found that the trapped gas fraction parameter and empirical expansion ratio substantially affect the accuracy of predictive modelling. Therefore, a new, comprehensive dynamic model that numerically simulates the bubble expansion process has been developed. This novel method directly determines the expansion ratio of the thermal conductivity model. The Eurocode is then used with multi-layer models to predict the steel temperature profile for a 1 h duration ISO fire. The accuracy is improved by modelling the temperatures and thermal resistances at the centre of each divided layer. The effects of different coatings and steel thicknesses are also investigated to provide the required data. The results are verified and validated by comparing them with the recent numerical and empirical results available in the literature. Full article

(This article belongs to the Section Mathematical Modelling and Numerical Simulation of Combustion and Fire)

►▼ Show Figures

Figure 1

23 pages, 3403 KB

Open AccessArticle

Rethinking Winter Heating in University Classrooms in China’s Hot Summer and Cold Winter Regions: Setpoint–Preference Mismatches, Pre-Heating, and Comfort Assessment

by Quyi Gong, Xin Ye, Xiaoyi Yang, Tao Zhang and Weijun Gao

Buildings 2026, 16(7), 1304; https://doi.org/10.3390/buildings16071304 (registering DOI) - 25 Mar 2026

Abstract

Winter thermal comfort in university classrooms in China’s Hot Summer and Cold Winter (HSCW) regions remains problematic due to mismatches between institutional heating setpoints and students’ actual thermal preferences. To investigate students’ thermal perceptions and behavioral responses, a post-occupancy evaluation (POE) survey was conducted, followed by field measurements in a typical classroom in Chengdu under three conditions: no-heating condition, heating conditions at 20 °C and 25 °C. Indoor environmental parameters were continuously monitored, and thermal comfort was assessed using the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) model. The results show that no-heating conditions were unacceptable, highlighting the necessity of heating. While the 20 °C setpoint provided partial improvement, thermal comfort was not consistently achieved throughout the day. In contrast, the 25 °C setpoint maintained near-neutral conditions during most occupied periods. In addition, a pre-heating duration of approximately 30 min was found to be essential for reducing initial thermal discomfort. Overall, the findings indicate that fixed institutional heating standards may not adequately satisfy students’ thermal needs. Adaptive heating strategies that combine appropriate setpoints with sufficient pre-heating duration are therefore recommended to balance thermal comfort and energy efficiency in university classrooms in the HSCW regions. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

►▼ Show Figures

Figure 1

22 pages, 2934 KB

Open AccessArticle

Design and Analytical Modeling of a Unidirectional Series Elastic Actuator with Tension-Spring-Based Rotational Stiffness Mechanism

by Deokgyu Kim, Jiho Lee and Chan Lee

Actuators 2026, 15(4), 180; https://doi.org/10.3390/act15040180 (registering DOI) - 25 Mar 2026

Abstract

This study proposes a tension-spring-based unidirectional rotational stiffness mechanism (TS-URM) and its implementation in a Unidirectional Series Elastic Actuator (USEA). Unlike conventional bidirectional rotary SEAs, the proposed design is structurally optimized for unidirectional torque transmission, improving deformation utilization efficiency in pulling-type applications. An analytical model was derived to establish the geometric relationship between spring elongation and rotational deformation, enabling explicit formulation of the torque–angle relationship. The influence of the installation angle on stiffness linearity was systematically analyzed, and a multilayer spring configuration was optimized to achieve a target rotational stiffness of approximately 42 Nm/rad. A preload adjustment mechanism was incorporated to eliminate nonlinear behavior in the initial operating region. Experimental results validated the analytical model and demonstrated stable unidirectional force control up to 130 N with steady-state errors within 1 N. The proposed mechanism provides predictable stiffness characteristics and an efficient structural solution for compact USEA systems. Full article

(This article belongs to the Special Issue Actuators in Robotic Control—3rd Edition)

12 pages, 3790 KB

Open AccessArticle

Bioinformatics and Preliminary Functional Analysis of OsPP2C61

by Hao Wang, Enjie Xu, Yujiao Shi, Nuoyan Li, Jinyilin Leng, Yuan Luo, Jianyang Sun, Yaofang Zhang and Zhongyou Pei

Genes 2026, 17(4), 374; https://doi.org/10.3390/genes17040374 (registering DOI) - 25 Mar 2026

Abstract

Background: Protein phosphatase 2Cs (PP2Cs) constitutes the largest phosphatase family in plants, playing a pivotal role in signal transduction. Within this family, the PP2C.D subfamily exerts significant influence on cell elongation and stress adaptation by mediating the ‘SAUR-PP2C.D-H+-ATPase’ regulatory module in the auxin signaling pathway. In rice, OsPP2C61 is a PP2C member whose molecular features and potential regulatory context remain unclear. Methods: Our study conducted a preliminary characterization of OsPP2C61 through integrated bioinformatics analysis, spatiotemporal expression profiling, and subcellular localization experiments in tobacco leaf cell. Results: OsPP2C61 encodes a 377-amino-acid protein predicted to be hydrophilic, basic, and structurally unstable. Secondary-structure prediction identified three major elements with random coils as the predominant component, whereas 3D modeling indicated alternating α-helices and β-sheets consistent with a canonical PP2C fold. Phylogenetic inference placed OsPP2C61 within the PP2C.D clade and revealed conserved motifs shared with OsPP2C25, OsPP2C28, and OsPP2C39. Promoter analysis showed enrichment of abscisic acid (ABA)- and methyl jasmonate (MeJA)-responsive elements along with multiple stress-related cis-regulatory motifs. Spatiotemporal expression analysis showed that OsPP2C61 is highly expressed in roots. Subcellular localization assays further demonstrated that the OsPP2C61-GFP fusion protein localizes to the nucleus and the plasma membrane when transiently expressed in epidermal cells of Nicotiana benthamiana. Conclusions: This work delivers the first comprehensive characterization of OsPP2C61, establishing a foundation for mechanistic studies and positioning OsPP2C61 as a candidate gene for rice improvement. Full article

(This article belongs to the Collection Feature Papers in Bioinformatics)

►▼ Show Figures

Figure 1

21 pages, 1231 KB

Open AccessReview

The Interconnection Between 3D and 4D Printing and Rheology: From Extrusion and Nozzle Deposition to Final Product Functionality

by Thomas Goudoulas and Theodoros Varzakas

Processes 2026, 14(7), 1055; https://doi.org/10.3390/pr14071055 (registering DOI) - 25 Mar 2026

Abstract

The successful application of 3D and 4D food printing is fundamentally governed by the rheology and microstructure of edible inks. These factors control every step, from extrusion and nozzle deposition to the final product functionality. This review systematically examines how formulation variables, including starch/protein composition, water content, and hydrocolloids, determine the network architecture and critical rheological properties, such as yield stress and viscoelasticity. These properties determine printing outcomes such as filament formation, stacking accuracy, and the stability of sensitive components. This review explores 4D printing as a “3D + 1D function,” where printed structures provide additional features over time, such as a controlled color change or bioactive release, while post-printing treatment often activates these features. Through case studies of novel inks, we show how interfacial chemistry and process parameters influence texture and stability. Finally, we discuss the application of rheological metrics for predicting printability and outline the critical need for developing multi-parameter, process-relevant printability indices to advance the field of digital food manufacturing. Full article

(This article belongs to the Special Issue Rheological Properties of Food Products)

►▼ Show Figures

Figure 1

13 pages, 633 KB

Open AccessArticle

Hematological Inflammatory Indices and the HALP Score for Pathogen Differentiation in Culture-Proven Late-Onset Neonatal Sepsis

by Aydin Bozkaya, Asli Okbay Gunes and Hatice Busra Kutukcu Gul

Children 2026, 13(4), 449; https://doi.org/10.3390/children13040449 (registering DOI) - 25 Mar 2026

Abstract

Objective: To evaluate the diagnostic and prognostic utility of the hemoglobin–albumin–lymphocyte–platelet (HALP) score and several systemic inflammatory indices derived from routine blood parameters—including the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), pan-immune inflammation value (PIV), and systemic inflammatory response index (SIRI)—for pathogen differentiation and clinical assessment in culture-proven late-onset neonatal sepsis (LOS). Methods: A retrospective analysis was conducted on a cohort of 150 neonates with culture-proven LOS. Systemic inflammatory indices were calculated at baseline (first week of life) and at the time of septic insult. The discriminative power of these indices was assessed via ROC curve analysis, with optimal cut-off points determined by the Youden Index. Risk stratification was performed using Odds Ratio (OR) modeling with 95% Confidence Intervals (CIs) to evaluate the predictive strength of each marker according to its respective threshold. Results: Diagnosis-phase assessments identified SII as the premier discriminator for microbiological etiology (AUC = 0.869; OR = 44.57), outperforming PLR and PIV. Although HALP demonstrated moderate efficacy in distinguishing pathogens, it lacked prognostic value regarding mortality. Conversely, SIRI displayed limited clinical utility, yielding the lowest predictive performance in our cohort. Conclusions: In neonatal sepsis, the HALP score provided additional clinical information when compared with several hematological inflammatory indices. Although HALP was not associated with mortality, prospective multicenter studies are needed to clarify the role of these cost-effective markers in pathogen differentiation and clinical assessment of LOS. Full article

(This article belongs to the Section Pediatric Neonatology)

15 pages, 3126 KB

Open AccessArticle

The Value of Multimodal Ultrasound in Differentiating Benign from Malignant Cytologically Indeterminate Thyroid Nodules

by Rong Yang, Yanfang Wang, Guo Chen, Xiaorong Lv, Yuanqing Zhang and Fang Nie

Cancers 2026, 18(7), 1071; https://doi.org/10.3390/cancers18071071 (registering DOI) - 25 Mar 2026

Abstract

Aim: To evaluate the diagnostic value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features in differentiating benign from malignant Bethesda III/IV thyroid nodules, and to identify independent predictors of malignancy. Methods: We retrospectively analyzed 164 surgically confirmed Bethesda III/IV thyroid nodules. CUS and CEUS features were evaluated by two experienced radiologists blinded to pathological outcomes. Univariate analysis compared features between benign and malignant groups. Multivariate logistic regression was used to identify independent predictors. Diagnostic models were constructed based on CUS alone, CEUS alone, and their combination, with performance evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each model. Results: The malignancy rate was 48.8% (80/164). Multivariate analysis identified microcalcifications (OR = 4.815, p < 0.001), aspect ratio >1 (OR = 2.499, p = 0.028), and irregular shape (OR = 2.465, p = 0.035) as independent risk factors, while older age (OR = 0.926 per year, p < 0.001) was protective. The CUS model achieved an AUC of 0.815 with high sensitivity (91.3%) and NPV (87.7%). The CEUS model performed poorly (AUC = 0.609). The combined model (AUC = 0.823) showed no significant improvement over CUS alone (p > 0.05). Physician subjective diagnosis based on CEUS TI-RADS yielded an AUC of 0.775. Conclusions: Conventional ultrasound features provide good diagnostic value for Bethesda III/IV nodules, with high sensitivity and NPV suitable for clinical screening. The addition of CEUS offered limited incremental benefit in this specific population, suggesting that the diagnostic value of CEUS for differentiating benign from malignant cytologically indeterminate thyroid nodules (ITNs) may be limited. Full article

(This article belongs to the Special Issue Application of Ultrasound in Cancer Diagnosis and Treatment)

17 pages, 5287 KB

Open AccessArticle

Predicting 10-Year Diabetes Risk Through Physiological Acceleration: A Longitudinal Deep Learning Ensemble Approach

by Sangsoo Kim, Seonghee Park, Jinmi Kim, Ha Jin Park, Soree Ryang, Myungsoo Im, Doohwa Kim and Kyeongjun Lee

Diagnostics 2026, 16(7), 992; https://doi.org/10.3390/diagnostics16070992 (registering DOI) - 25 Mar 2026

Abstract

Background/Objectives: Type 2 diabetes (T2D) develops gradually over many years through a prolonged preclinical phase, yet traditional static risk scores often fail to capture these dynamic metabolic trajectories. We propose a longitudinal deep learning framework to predict the 10-year risk of Type 2 diabetes onset defined by comprehensive ADA criteria by modeling the physiological acceleration of routine clinical biomarkers. Methods: Utilizing an 18-year longitudinal dataset from the community-based Korean Genome and Epidemiology Study (KoGES) cohort, we selected

N = 4354

participants with complete follow-up records, ensuring high data integrity without requiring synthetic data augmentation. We constructed a 3-dimensional tensor of 21 non-invasive clinical variables spanning a 6-year observation window. To resolve the inherent precision-recall trade-offs of individual models, we developed a stacking ensemble that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures via a logistic regression meta-learner. To evaluate the added value of longitudinal modeling, we compared this dynamic framework against a static XGBoost baseline that only saw the most recent data. Results: Evaluated on an independent test set (

n = 874

), the ensemble significantly outperformed baseline models, achieving an overall accuracy of 0.90 (95% CI: 0.88–0.92) and an AUROC of 0.94 (95% CI: 0.93–0.95). By harmonizing LSTM’s sensitivity and GRU’s precision, the model yielded an exceptional Positive Predictive Value (PPV) of 0.97, a sensitivity of 0.80, and a specificity of 0.98. Conclusions: This framework provides a highly accurate, resource-efficient triage instrument for T2D screening, thereby reducing unnecessary clinical alerts and improving screening efficiency. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Medicine—2nd Edition)

19 pages, 1120 KB

Open AccessArticle

UHPC Creep Behavior and Neural Network Prediction with Calibration of fib Model Code 2020

by Shijun Wang, Mengen Yue, Wenming Zhang and Teng Tong

Buildings 2026, 16(7), 1300; https://doi.org/10.3390/buildings16071300 (registering DOI) - 25 Mar 2026

Abstract

Ultra-High-Performance Concrete (UHPC) is increasingly used in slender and prestressed structural members due to its superior strength and durability. However, inaccurate or incomplete prediction of creep deformation may lead to excessive long-term deflection, prestress loss, cracking, and potential serviceability or safety risks in buildings and infrastructure. Therefore, reliable prediction methods for UHPC creep are essential for both structural design and long-term performance assessment. In this study, a database containing 60 literature-derived UHPC creep records was compiled to investigate the creep coefficient at approximately 100 days. Pearson correlation analysis revealed strong interdependence among predictors and weak single-variable linear relationships, indicating that creep behavior is governed by nonlinear interactions. A feedforward backpropagation neural network (BPNN) trained using the Levenberg–Marquardt algorithm was developed to predict the creep coefficient. To maintain engineering interpretability, the fib Model Code 2020 (MC2020) formulation was adopted as a code-based benchmark and further calibrated using ridge regression. Results show that the calibrated MC2020 model improves prediction consistency, while the BPNN model provides the highest predictive accuracy. The proposed framework integrates machine-learning prediction with interpretable code-based calibration, contributing to the development of creep modeling approaches for UHPC and providing practical support for the safe design of UHPC structures. Full article

(This article belongs to the Section Building Materials, and Repair & Renovation)

22 pages, 8228 KB

Open AccessArticle

Bridging Interfaces and Morphology: A Mesoscale Dynamics Framework for Predicting Percolation in Organic Solar Cells

by Estela Mayoral-Villa and Alfonso R. García-Márquez

Energies 2026, 19(7), 1624; https://doi.org/10.3390/en19071624 (registering DOI) - 25 Mar 2026

Abstract

The dynamic self-assembly and phase separation of donor–acceptor blends are processes that dictate the nanoscale morphology in organic solar cells. Here, we employ a fluidics-inspired framework, integrating dissipative particle dynamics simulations with percolation theory, to investigate the morphogenesis of two non-fullerene systems: P3HT-PPerAcr and P3HT-PFTBT. We analyze monomeric and homopolymer blends, and copolymer macrostructures, focusing on how key parameters such as temperature and polymer chain flexibility govern the dynamic evolution towards percolating networks. Our simulations captured the fundamental fluidic behavior and universal scaling near the critical percolation threshold (χ_c). The critical exponent β revealed distinct universality classes dictated by system compatibility and flexibility: monomeric and flexible homopolymer blends below the critical temperature (T_c) exhibit mean field behavior (β ≈ 1). In contrast, monomeric systems above χ_c and flexible copolymers below χ_c display 3D percolation behavior (β ≈ 0.45). In the case of flexible copolymeric macromolecules, above percolation threshold a quasi-bidimensional behavior emerge with (β ≈ 0.1). Notably, semi-rigid and rigid homopolymeric and copolymeric linear architectures induce a dimensional crossover, yielding quasi-2D (β ≈ 0.14) and quasi-1D (β ≈ 0.0) morphologies. These findings establish a direct link between tunable fluidic interactions, chain dynamics, and the emergence of optimal bicontinuous percolation networks. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

►▼ Show Figures

Figure 1

32 pages, 1343 KB

Open AccessReview

Hierarchical Model Predictive Control with Inferential Soft Sensing for Stabilizing Thermal Gradients in Agricultural Biomass Gasification

by Tudor Octavian Pocola, Florin Ioan Bode and Otto Lorand Rencsik

Processes 2026, 14(7), 1053; https://doi.org/10.3390/pr14071053 (registering DOI) - 25 Mar 2026

Abstract

Decentralized agricultural gasification remains constrained by the thermochemical instability of high-alkali residues, such as straw and stalks. This operational bottleneck is defined by a narrow thermal window: oxidation core temperatures are typically targeted above 1000 °C for effective tar cracking, yet grate temperatures are constrained, often below 850 °C, depending on the specific ash fusion characteristics of the feedstock, to prevent viscous sintering and bed clinkering. This work proposes a conceptual framework for a control strategy designed to address these conflicting requirements through a unified framework integrating inferential soft-sensing, hierarchical Model Predictive Control (MPC), and sensor health monitoring. Machine learning architectures capture temporal dependencies and cumulative thermochemical transformations to reconstruct unobservable internal states. This enables real-time state estimation with reported accuracy levels (average test R² of 0.91–0.97) and 100% physical consistency through monotonicity constraints, effectively managing the critical thermal lag of densified pellets (400–600 s response time). High-fidelity CFD simulations anchor the soft-sensing layer, ensuring model robustness across the inherent variability of agricultural feedstocks. The architecture shifts control logic from reactive adjustments to anticipatory intervention through adaptive multi-mode operation that decouples high-intensity oxidation from grate integrity limits, while dynamic biochar management serves as a multifunctional control variable for tar cracking enhancement and alkali sequestration. Future work will focus on pilot-scale validation under transient feedstock conditions. Full article

(This article belongs to the Special Issue Progress on Solid Fuel Combustion, Pyrolysis and Gasification)

►▼ Show Figures

Figure 1

34 pages, 10530 KB

Open AccessArticle

Approximate Analytical Solution for Longitudinal Stress in U-Shaped Aqueducts Induced by Circumferential Tensioning

by Heng Min, Yuhang Chen and Jian Wang

Appl. Sci. 2026, 16(7), 3173; https://doi.org/10.3390/app16073173 (registering DOI) - 25 Mar 2026

Abstract

During circumferential tensioning of prestressing strands in U-shaped aqueducts, longitudinal tensile stresses may develop and impair crack resistance. Most existing studies rely on three-dimensional finite element (FE) simulations. Although accurate, FE modeling is time-consuming and unsuitable for rapid scheme evaluation during construction. To overcome this limitation, the U-shaped aqueduct was first simplified as a cylindrical shell and the feasibility of this idealization was verified. An approximate analytical solution was then derived from cylindrical shell theory to predict the longitudinal stress induced by circumferential prestressing. Practical factors, including non-uniform wall thickness, non-equidistant strand spacing, and strand positional deviations, were incorporated to improve engineering applicability. FE results confirm good agreement, with RMSE of 0.055–0.169 MPa and NRMSE of 2.3–19.6%, where the upper bound occurs only in localized regions. The method was further applied to an engineering project to optimize the tensioning scheme. With a rational interval-tensioning procedure, the peak longitudinal tensile stress was reduced by 31.6%. Overall, the proposed approach enables rapid stress estimation and supports preliminary screening and optimization of circumferential tensioning schemes. Full article

(This article belongs to the Special Issue Computational Analysis and Integrity Assessment of Engineering Structures)

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 2000.

Go to page 1 2 3 4 5

Search Results (183,693)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI