Search Results (459)

To prolong the service life of asphalt pavement and reduce its maintenance cost, a fiber Bragg grating (FBG) sensor encapsulated in carboxylated carbon nanotube (CNT-COOH)-modified gel material suitable for strain monitoring of asphalt pavement was developed. Through tensile and bending tests, the effects of carboxylated carbon nanotubes on the mechanical properties of gel materials under different dosages were evaluated and the optimal dosage of carbon nanotubes was determined. Infrared spectrometer and scanning electron microscopy were used to compare and analyze the infrared spectra and microstructure of carbon nanotubes before and after carboxyl functionalization and modified gel materials. The results show that the incorporation of CNTs-COOH increased the tensile strength, elongation at break, and tensile modulus of the gel material by 36.2%, 47%, and 17.2%, respectively, and increased the flexural strength, flexural modulus, and flexural strain by 89.7%, 7.5%, and 63.8%, respectively. Through infrared spectrum analysis, it was determined that carboxyl (COOH) and hydroxyl (OH) were successfully introduced on the surface of carbon nanotubes. By analyzing the microstructure, it can be seen that the carboxyl functionalization of CNTs improved the agglomeration of carbon nanotubes. The tensile section of the modified gel material is rougher than that of the pure epoxy resin, showing obvious plastic deformation, and the toughness is improved. According to the data from the calibration experiment, the strain and temperature sensitivity coefficients of the packaged sensor are 1.9864 pm/μm and 0.0383 nm/°C, respectively, which are 1.63 times and 3.61 times higher than those of the bare fiber grating. The results of an applicability study show that the internal structure strain of asphalt rutting specimen changed linearly with the external static load, and the fitting sensitivity is 0.0286 με/N. Combined with ANSYS finite element analysis, it is verified that the simulation analysis results are close to the measured data, which verifies the effectiveness and monitoring accuracy of the sensor. The dynamic load test results reflect the internal strain change trend of asphalt mixture under external rutting load, confirming that the encapsulated FBG sensor is suitable for the long-term monitoring of asphalt pavement strain. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

This study investigated the fracture behavior of asphalt mixtures under indirect tensile loading by comparing the performance of homogenized and micromechanical finite element (FEMs) models based on the cohesive zone model (CZM). Five asphalt mixture types were tested experimentally, and both models were calibrated and validated using load–displacement curves from indirect tensile tests (IDTs). The micromechanical model, incorporating random aggregate generation and three-phase material definition, exhibited significantly higher predictive accuracy (R² = 0.86–0.98) than the homogenized model (R² = 0.66–0.77). The validated micromechanical model was further applied to quantify the impact of construction-related deficiencies—namely, increased air voids, non-continuous gradation, and aggregate segregation. The simulation results showed that higher void content (from 4% to 10%) reduced peak load by up to 35% and increased localized stress concentrations by up to 40%. Discontinuous gradation and uneven aggregate distribution also led to premature crack initiation and more complex fracture paths. These findings demonstrated the value of micromechanical modeling for evaluating sensitivity to mix design and compaction quality, providing a foundation for performance-based asphalt mixture optimization and durability improvement. Full article

The performance of SBS (Styrene Butadiene Styrene) modified asphalt mixtures can be enhanced through the addition of fibers including basalt, ceramic, and glass. This study investigates whether a reduced SBS content of 3%, combined with 0.3% fiber reinforcement can match or exceed the performance of a traditional 7% SBS mixture. A comparative analysis was carried out by examining both performance efficiency and life cycle costs across ceramic, basalt, and glass fiber-reinforced mixtures. Maintenance requirements for each scenario were factored into the life cycle analysis. To assess structural integrity, 3D finite element simulations were conducted using the Burger’s logit model while focusing on fatigue and rutting damage. Findings indicate that basalt and ceramic fiber mixtures deliver better asphalt mixtures, thereby outperforming the 7% SBS mix by requiring fewer maintenance interventions. However, due to the higher cost of ceramic fiber mixtures at 831 Eur/m³, basalt fiber emerges as the more cost-effective option, achieving a performance efficiency gain of 20% with reduced costs at 532 Eur/m³. Among the fiber-reinforced variants, glass fiber showed the least improvement in performance, with a difference in 11% and 13% when compared to ceramic fiber and basal fiber, respectively. Full article

In this study, software add-ins were developed and presented to allow data processing and statistical analysis of the unique shape of cotton fiber length distribution. The approach uses VBA coding in Excel to process the data, as well as the JMP 14-17 application and add-in builder tools to fit finite mixture models to empirical fiber length distributions. The resulting model derives a parametric expression for the fiber length probability density function. The analysis add-in was applied and validated on a wide range of empirical length distributions and proved to parameterize the complex distribution patterns with an excellent goodness of fit. Both tools were compiled into installable add-ins that extended the capabilities of MS Excel for the processing of AFIS distribution reports and the statistical toolbox of JMP using the Application Builder JSL coding. Installable add-ins, along with a user manual, are available for download by cotton researchers. Full article

Epoxy Resin System (ERS) steel bridge pavement, which comprises a resin asphalt (RA) base layer and a modified asphalt wearing course, offers cost efficiency and rapid installation. However, the combined effects of traffic loads and environmental conditions pose significant challenges, requiring greater high-temperature stability than conventional pavements. The thermal sensitivity of resin materials and the use of conventional asphalt mixtures may weaken deformation resistance under elevated temperature conditions. This study investigates the thermal field distribution and high-temperature performance of ERS pavements under extreme conditions and explores temperature reduction strategies. A three-dimensional thermal field model developed using finite element analysis software analyzes interactions between the steel box girder and pavement layers. Based on simulation results, wheel tracking and dynamic creep tests confirm the superior performance of the RA05 mixture, with dynamic stability reaching 23,318 cycles/mm at 70 °C and a 2.1-fold improvement in rutting resistance in Stone Mastic Asphalt (SMA)-13 + RA05 composites. Model-driven optimization identifies that enhancing internal airflow within the steel box girder is possible without compromising its structural integrity. The cooling effect is particularly significant when the internal airflow aligns with ambient wind speeds (open-girder configuration). Surface peak temperatures can be reduced by up to 20 °C and high-temperature durations can be shortened by 3–7 h. Full article

This paper analyzed the behavior of polymer composite materials reinforced with randomly oriented short natural fibers (hemp, flax, etc.) subjected to external stresses under quasistatic contact conditions with dry Coulomb friction. We presumed the composite body, a 2D flat rectangular plate, being in frictional contact with a rigid foundation for the quasistatic case. The manuscript proposes the finite element method approximation in space and the finite difference approximation in time. The problem of quasistatic frictional contact is described with a special finite element, which can analyze the state of the nodes in the contact area, and their modification, between open, sliding, and fixed contact states, in the analyzed time interval. This finite element also models the Coulomb friction law and controls the penetrability according to a power law. Moreover, the quasi-static case analyzed allows for the description of the load history using an incremental and iterative algorithm. The discrete problem will be a static and nonlinear one for each time increment, and in the case of sliding contact, the stiffness matrix becomes non-symmetric. The regularization of the non-differentiable term comes from the modulus of the normal contact stress, with a convex function and with the gradient in the sub-unit modulus. The non-penetration condition was achieved with the penalty method, and the linearization was conducted with the Newton–Raphson method. Full article

We propose a novel framework for adaptive stochastic dynamics analysis of tourist behavior by integrating context-aware Markov models with finite mixture models (FMMs). Conventional Markov models often fail to capture abrupt changes induced by external shocks, such as event announcements or weather disruptions, leading to inaccurate predictions. The proposed method addresses this limitation by introducing virtual sensors that dynamically detect contextual anomalies and trigger regime switches in real-time. These sensors process streaming data to identify shocks, which are then used to reweight the probabilities of pre-learned behavioral regimes represented by FMMs. The system employs expectation maximization to train distinct Markov sub-models for each regime, enabling seamless transitions between them when contextual thresholds are exceeded. Furthermore, the framework leverages edge computing and probabilistic programming for efficient, low-latency implementation. The key contribution lies in the explicit modeling of contextual shocks and the dynamic adaptation of stochastic processes, which significantly improves robustness in volatile tourism scenarios. Experimental results demonstrate that the proposed approach outperforms traditional Markov models in accuracy and adaptability, particularly under rapidly changing conditions. Quantitative results show a 13.6% improvement in transition accuracy (0.742 vs. 0.653) compared to conventional context-aware Markov models, with an 89.2% true positive rate in shock detection and a median response latency of 47 min for regime switching. This work advances the state-of-the-art in tourist behavior analysis by providing a scalable, real-time solution for capturing complex, context-dependent dynamics. The integration of virtual sensors and FMMs offers a generalizable paradigm for stochastic modeling in other domains where external shocks play a critical role. Full article

Accurately measuring strain in asphalt pavements using buried strain sensors remains challenging due to the temperature sensitivity and heterogeneity of asphalt mixtures. This study focuses on improving the measurement performance of buried strain sensors in asphalt mixtures through finite element simulations. First, the sensing errors of existing buried strain sensors in asphalt mixtures were analyzed based on laboratory experiments. Subsequently, the factors affecting the deformation compatibility between the sensor and the asphalt mixture were investigated, and the effect of asphalt mixture heterogeneity on the stability of the sensor measurements are discussed. More importantly, a series of optimization strategies for buried strain sensors are proposed. The findings suggest that the equivalent modulus of the buried strain sensor should closely match that of the asphalt mixture, and its encapsulation must avoid inducing any reinforcement effects. Considering the dynamic modulus range of the asphalt mixture, it is recommended to adopt the lower bound, such as 0.25 GPa, as the equivalent modulus of the buried sensor. To eliminate the stiffening effect, the encapsulation may utilize low-modulus flexible materials. The inherent heterogeneity of asphalt mixtures influences the measurement stability of buried strain sensors: a higher overall modulus leads to a more uniform internal strain distribution, whereas a larger nominal maximum aggregate size (NMAS) results in poorer strain field uniformity. Increasing the gauge length of the buried strain sensor to at least three times the NMAS significantly enhances measurement stability. This study provides valuable guidance for the design of buried strain sensors in asphalt pavement applications. Full article

Lightweight artificial aggregates (LWAs) are widely used in civil construction, but their conventional production depends on pure clays, a finite natural resource that negatively impacts the environment. This study aims to contribute to minimizing this issue by exploring the use of sustainable ternary mixtures of kaolinitic clay (KC), chamotte residues (CHT), and eucalyptus firewood ash (EFA), promoting a more environmentally friendly approach to the manufacture of LWAs. Thus, the aim was to develop and optimize LWAs using different replacements of industrial waste. Furthermore, the Taguchi method is employed to identify the optimal manufacturing parameters, such as waste content, sintering temperature, and heating time. The research involved the production of 32 distinct mixtures with different proportions of KC, CHT, and EFA, processed through grinding and sintering at temperatures ranging from 1075 °C to 1180 °C. The samples were evaluated for density, water absorption, mechanical strength, and expansion index. Statistical analysis was conducted using ANOVA to validate the most significant factors. The results revealed that mixtures with 80% of waste presented an aggregate expansion index of up to 60%, a minimum bulk density of 1.20 g/cm³ (which aligns with requirements for structural applications but exceeds the maximum bulk density for some lightweight aggregates), and crushing strength higher than 5 MPa, satisfying the normative criteria for commercial LWAs. In addition, 63 industrial applications were identified for the developed materials, ranging from structural lightweight concretes to thermal and acoustic insulation with varied microstructures. Therefore, the partial replacement of clay by CHT and EFA waste represents a promising alternative for producing sustainable LWAs, helping to reduce environmental impacts while providing quality materials for various applications in the most diverse industrial sectors. Full article

This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry. Full article

In questionnaires, respondents sometimes feel uncertain about which category to choose and may respond randomly. Including uncertainty in the modeling of response behavior aims to obtain more accurate estimates of the impact of explanatory variables on actual preferences and to avoid bias. Additionally, variables that have an impact on uncertainty can be identified. A model is proposed that explicitly considers this uncertainty but also allows stronger certainty, depending on covariates. The developed uncertainty rating scale model is an extended version of the adjacent category model. It differs from finite mixture models, an approach that has gained popularity in recent years for modeling uncertainty. The properties of the model are investigated and compared to finite mixture models and other ordinal response models using illustrative datasets. Full article

This study explores the use of recycled brick powder (P_RB), derived from waste bricks, and calcined recycled slurry powder (P_CRS), sourced from waste cement blocks, as partial replacements for cement and fly ash in concrete. These materials can be utilized to produce concrete with favorable engineering properties. Five concrete mixtures with varying P_RB/P_CRS proportions were prepared. Uniaxial monotonic compression tests were conducted to generate stress-strain curves for each mixture. Corresponding physical superposed slabs were fabricated, and finite element (FE) models were developed for each slab. Both physical testing and explicit FE simulations were performed to evaluate the flexural performance of the slabs. The results demonstrated that the flexural performance of the P_RB/P_CRS recycled micro-powder concrete slabs was comparable to that of conventional concrete slabs. Notably, the slab incorporating a 1:1 mixture of P_RB and P_CRS instead of fly ash exhibited the highest yield and ultimate bearing capacities, reaching 99.3% and 98.4% of those of the conventional concrete slab, respectively. The FE simulations accurately predicted the flexural performance, with maximum deviations of 8.9% for the yield load and 6.5% for the ultimate load. Additionally, the simulation-based energy time-history curve provides valuable insights into the progression of slab cracking. This study contributes to the advancement of research on the engineering and mechanical performance of concrete members incorporated with P_RB/P_CRS. Full article

To investigate the influence of pavement texture on tire hydroplaning, this study utilized laser scanning to capture the surface characteristics of three asphalt mixtures—AC-13, SMA-13, and OGFC-13—across fifteen rutting plate specimens. Three-dimensional (3D) pavement models were reconstructed to incorporate realistic texture data. Finite element simulations, employing fluid-structure interaction and explicit dynamics in Abaqus, were conducted to model tire-water-pavement interactions. The results indicate that the anti-skid performance ranks as OGFC > SMA > AC. However, despite OGFC and SMA exhibiting comparable anti-skid metrics (e.g., pendulum friction value and mean texture depth), OGFC’s superior texture uniformity results in significantly better hydroplaning resistance. Additionally, tire tread depth critically influences hydroplaning speed. A novel Anti-Slip Comprehensive Texture Index (ACTI) was proposed to evaluate pavement texture uniformity, providing a more comprehensive assessment of anti-skid performance. These findings underscore the importance of texture uniformity in enhancing pavement safety under wet conditions. Full article

Springback control is a critical factor in profile stretch-bending-torsion forming. A new stretch-bending-torsion automatic forming method based on the mixture of finite element and BP neural network (FE-BPNN) is proposed. The method enhances the shape accuracy of profiles after single-step forming. Initially, the study introduces the 3D multi-point stretch-bending and torsion (3D MPSBT) forming machine and its forming principles. Subsequently, it details the springback prediction method and automatic forming control approach based on BPNN. A springback control model is established through numerical simulation and experiments. The proposed springback control method is compared with a springback factor-based approach from other researchers using hollow rectangular profiles undergoing combined bending and torsion deformation as the research object. The results validate the effectiveness and advantages of the proposed method. Full article