Asphalt mixtures are complex and heterogeneous materials whose performance is governed by their intricate mesostructure and multiphase interactions. Three-dimensional (3D) numerical simulation has emerged as a powerful tool for evaluating the structure and mechanical response of asphalt mixtures. This paper synthesizes recent advances in 3D mesoscopic simulation of asphalt mixtures, distinguishing the distinctions and benefits of image-based and user-defined 3D model generation techniques. This paper identifies the important parameters that affect the reliability of 3D models, encompassing aggregate parameters, asphalt mortar parameters and air void parameters. Furthermore, it outlines the advantages and disadvantages of mainstream 3D simulation methods for asphalt mixtures, along with recent developments in multi-method coupled 3D simulation. Finally, the process and challenges of model validation are discussed. Future research should focus on the precise characterization of aggregate size, morphology, and aggregate–aggregate contact models. The development of reusable digital aggregate libraries is essential to enhance the realism and efficiency of 3D model generation. Improvements in coupled simulation techniques are also needed to ensure interface data consistency and force–displacement synchronization. Moreover, researchers should provide more comprehensive documentation of parameter calibration and iterative optimization processes.
Measuring pavement surface deflections using a falling weight deflectometer (FWD) is a common technique to assess structural conditions and guide pavement maintenance decisions. However, FWD deflections in flexible pavements are highly sensitive to temperature variations due to the viscoelastic nature of asphalt. An accurate analysis of these deflections requires adequate correction to a reference temperature, typically using the method outlined in the AASHTO 1993 design guide. Despite its widespread use, the specific input asphalt temperature for this correction is not well-defined. In this study, temperature sensors were installed at various depths in three full-depth asphalt pavement sections in Indiana (USA), allowing for the measurement of temperature gradients during FWD tests conducted at different times throughout the day. The study evaluated the accuracy of deflection corrections when using different temperature correction factors, calculated using measured temperatures at various depths following the AASHTO 1993 guidelines. The results demonstrate that using the pavement temperature at 100 mm depth provided more accurate deflection corrections than using surface or mid-depth temperatures in full-depth asphalt pavements.
This study integrates Bayesian optimization (BO) with the natural gradient boosting (NGBoost) algorithm to accurately predict aeolian sand concrete (ASC) compressive strength. The main results are summarized as follows. 1) The NGBoost model demonstrates high precision in predicting ASC compressive strength, achieving testing set metrics with a coefficient of determination of 0.945, mean squared error of 4.145 MPa2, and root mean squared error of 2.036 MPa. 2) Feature importance ranking from the NGBoost model identifies age as the significant factor influencing ASC compressive strength, while the effects of aeolian sand ratio, water-to-binder ratio (W/B), and coarse aggregate are minimal. 3) SHapley Additive exPlanations (SHAP) analysis indicates a positive correlation between age, cement, coarse aggregate, superplasticizer, and the compressive strength of ASC. In contrast, the aeolian sand ratio, W/B, and fine aggregate show negative correlations. 4) A python-based graphical user interface (GUI) has been developed to enable engineers to predict ASC compressive strength efficiently, thus enhancing the model’s practical application.
The reliable prediction of hoop strain of fiber-reinforced polymer (FRP)-confined concrete is crucial for assessing confinement efficiency and ensuring structural integrity. Existing empirical models often fall short as a result of idealized assumptions and limited generalizability across diverse materials and geometries. This study presents a novel, data-driven machine learning (ML) approach to estimate the effective hoop strain of FRP-confined circular concrete columns. A refined database comprising 309 experimental specimens, including Carbon, glass, and aramid FRPs, was used. Eight ML algorithms, encompassing both single (K-Nearest Neighbors, Kernel Ridge Regression, Support Vector Regression, Decision Tree) and ensemble (AdaBoost, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest) models, were trained and optimized using Optuna with 10-fold cross-validation. The top-performing models have coefficient of determination of greater than 95% as well as low residual variance and error on the full data set. Accordingly, SHapley Additive exPlanations were incorporated for global and local interpretability of the model predictions. The best-performing model was deployed in a user-friendly graphical interface, aiding an accurate and interpretable tool for practitioners. The proposed framework significantly outperforms conventional empirical models, offering a scalable solution for assessing hoop strain of FRP-confined concrete.
It was a challenge to monitor concrete structure crack under complex environmental action. To obtain conductive hydrogel material with higher stretchability, signal response sensitivity and stability to monitor concrete structure cracking, the conductive hydrogel reinforced by different content of cellulose nanocrystals, which is called polyacrylic acid-cellulose nanocrystals (PAA-CNC), were developed in this paper. The performance improvement of PAA-CNC was studied by scanning electron microscope, resistance, uniaxial tensile and cyclic tensile test. Finally, the concrete crack monitoring accuracy of PAA-CNC was verified by three-point bend loading test. The result showed that combining cellulose nanocrystals with hydroxyl in conductive hydrogels can form uniformly dispersed micelles and three-dimensional network structure, which can increase the ionic conductive path and connection strength between molecules. When cellulose nanocrystals content of hydrogel was 0.12%, the effective strain sensing range and sensitivity within the range reached the maximum. When the content of cellulose nanocrystals was 0.12, the effective strain sensing range and sensitivity of PAA-CNC will reach maximum value. Compared with other contents of cellulose nanocrystals, PAA-CNC0.12 can produce a stable signal response when tested and quickly recover to the initial resistance after cyclic stretching. The crack width obtained by PAA-CNC0.12 does not exceed 5% of that obtained by digital image correlation equipment.
This study introduces a powerful automated regression workflow (ARW) for accurately predicting deflection in bio-inspired laminated composite plates using diverse machine learning (ML) algorithms. The ARW significantly automates complex processes like hyperparameter optimization, model training, and performance evaluation, accelerating analytical insights. Six different ML regression models were systematically deployed, achieving an impressive average prediction accuracy, five models exceeding 99%, on a comprehensive finite element-generated data set. Notably, the eXtreme gradient boosting regression (XGBR) model exhibited superior performance (R2 = 0.999, MAE = 0.010, RMSE = 0.013) on unseen data. Interpretability analyses using SHapley Additive exPlanations and local interpretable model-agnostic explanations on the optimal XGBR model consistently identified boundary conditions and the ratio of elastic moduli (E1/E2) as the most influential factors, followed by the aspect ratio (a/h) and loading type. This work establishes an efficient, accurate, and interpretable framework that accelerates the design and fundamental understanding of these complex composite structures, which can be further applied to numerous applications.
This study investigates the multifield vibration behavior of a porosity-dependent bidirectional functionally graded piezoelectric nano-plate (FGPN) subjected to hygrothermal and thermoelectric loading. The material composition is defined by sigmoid and power-law distributions along both transverse and axial directions, accommodating even, uneven, and symmetrically centered porosity patterns. The model incorporates nonclassical elasticity theory and von Kármán nonlinear strains, with the governing equations formulated using a modified first-order shear deformation theory and derived through the energy principle. A higher-order finite element formulation, coupled with a modified Newton–Raphson procedure, ensures robust computational accuracy, validated through convergence tests. The analysis delves into the influence of porosity distribution, bidirectional material variations, non-uniform thickness, thickness ratios, variable elastic foundations, and boundary conditions on vibrational behavior. Additionally, the study explores the interplay of hygrothermal and electrical loading conditions in diverse configurations. The findings highlight the pivotal role of bidirectional material gradation in shaping the vibrational response of porous FGPN structures, offering valuable insights for the design of nano-plates in hygrothermal and thermoelectric applications.
The self-centring brace is recognized as one of the practical solutions for mitigating catastrophic consequences caused by earthquakes and improving structural resilience. Compared to the current methods where self-centring capacity is typically provided by pre-stressed steel rods or disc springs, carbon fiber-reinforced polymer (CFRP) material of higher tensile strength and deformation capacity is emerging as a preferred alternative to traditional materials. Based on that, this study mainly aims to propose a novel self-centring buckling-restrained brace (SC-BRB) by using pre-stressed CFRP rods as self-centring components, named the CFRP-SC-BRB. First, component-level analysis was conducted by experimental and numerical methods, to verify the feasibility of the designed configuration. Cyclic and ultra-low-cycle fatigue tests on the specimen demonstrated the excellent performance of the CFRP-SC-BRB, with the peak force of the brace at the drift ratio of 1/120 over 2900 kN and a residual drift ratio controlled below 0.5%. Finite element models in refined and simplified methods were validated by the experimental results and theoretical prediction. Then, a series of system-level analyses are carried out on a prototype frame incorporating the proposed CFRP-SC-BRBs. Compared to the original design with conventional BRBs, seismic responses of the frame fully or partially replaced by the SC-BRBs show a competitive advantage in seismic performance. Especially for the SC-BRB frame with full replacement, the median residual inter-storey drift ratios are reduced by 29.3% and 50.5% under design basis and maximum considered earthquakes, respectively, compared to the conventional BRB frame. In conclusion, it is demonstrated that the proposed CFRP-SC-BRB is effective in improving seismic resilience both at component and system levels. Practical suggestions are also provided to address potential challenges in promoting the novel product in actual application.
Skirted foundations are usually used in marine engineering. More researches revealed that the variations in soil undrained shear strength considerably influence the assessing performance of the bearing capacity of skirted foundations. This study proposes two machine learning-based methods to predict safety factors () of skirted foundations under combined loadings. By comparing the prediction performance of models based on Convolutional Neural Networks (CNN) and Gaussian Process Regression, this study investigates the effect of input size of soil random field on prediction accuracy and identifies the optimal CNN model. The proposed CNN model efficiently predicts corresponding safety factors for different combined loadings under various soil random fields, achieving similar accuracy to the traditional time-consuming random finite element. Specifically, the coefficient of correlation exceeds 0.93 and the mean relative error is less than 2.8% for the variation of the horizontal scales of fluctuation under different combined loadings. The relative error of the predicted value is less than 3.00% given three failure probabilities considering the variation of the vertical scales of fluctuations. These results demonstrate satisfactory prediction performance of the proposed CNN model.
The rapid development of global transportation infrastructure has led to increasing applications of large-span shallow-buried tunnels in complex geological conditions. Shallow-buried twin-arch tunnels with asymmetric overburden are prone to deformation and instability under long-term rainfall, particularly in soft clayey strata. This study investigates the rainfall-induced deformation behavior of such tunnels through a combined physical modeling and numerical simulation approach, based on the Wulongshan Tunnel in Nanjing. Results show that rainfall primarily affects shallow slopes within the first 3 d, with delayed seepage responses and infiltration depths up to twice the tunnel diameter. Under sustained torrential rainfall, crown settlement increases by 50% compared to dry conditions, while surface deformation exceeds twice the dry-state value and surpasses crown settlement. A coupled seepage–deformation model incorporating strength softening captures a “slow–rapid–slow” settlement pattern with increasing rainfall and highlights elevated deep-seated sliding risk under extreme conditions. The findings clarify the deformation mechanisms of twin-arch tunnels under rainfall and provide a basis for support design, construction timing, and risk control in similar geotechnical environments.