ABSTRACT The hostile environments inevitably leave the polyethylene (PE) components vulnerable to such volumetric flaws as localized material loss, which poses a severe threat to structural integrity and safety. The co-polarization reflectometry is one of the modes of microwave testing for detection and imaging of volumetric flaws, particularly those occurring at the back surface of the PE component. However, its testing result would be influenced by the variation in stand-off distance between the antenna aperture and the structure surface. In view of this, the cross-polarization reflectometry (CRPR) for screening the back-surface material loss in a PE slab is intensively investigated. The response characteristics of the cross-polarization reflected signal to the defect and the resultant testing signal are analyzed through simulations and experiments. Following the raw image produced with the CRPR, an image processing method for recovering the defect image insensitive to the stand-off variation is proposed. The image segmentation is subsequently applied to the recovered images for evaluation of the defect opening area. Through comparison with the true defect image and opening area, it is affirmed that the proposed cross-polarization microwave screening method alongside the defect-image recovery algorithm is capable of enhancing the quality of imaging of the back-surface volumetric flaws in PE slabs, in terms of the testing sensitivity and image contrast.
ABSTRACT Broken-yarn detection in pull-winding and pultrusion processes still relies mainly on manual inspection, whereas yarn overlap, blurred defect edges, and vibration-induced background disturbances hinder reliable visual analysis. To address these problems, a broken-yarn defect detection method combining feature enhancement, three-frame differencing, and deep-learning recognition is proposed. A Gaussian-curvature-based image scaling method is used to preserve fine geometric textures, and a DGSWLTLBO-Otsu segmentation algorithm is introduced to improve broken-yarn region extraction under complex backgrounds. Based on enhanced differential images, a DF-Half-AlexNet model is developed for broken-yarn recognition. Experimental results show that the proposed method improves edge-detail preservation and broken-yarn region extraction, and the DF-Half-AlexNet model achieves an accuracy of 97.15 % ± 0.21 %. Vibration experiments further demonstrate its recognition stability under interference conditions.
ABSTRACT This study investigates the dynamic stability of frozen soil slopes subjected to strong disturbances such as blasting. Dynamic compression tests were conducted on saturated frozen clay specimens using a split Hopkinson pressure bar system. The effects of strain rate (100–700 s−1), temperature (−15°C, −23°C, and −30°C), and pore ratio (0.30, 0.24, and 0.18) on the mechanical behavior were systematically examined. The propagation of stress waves and the dynamic stress–strain responses were analyzed. Results show that as the strain rate increases, the arrival times of the incident, reflected, and transmitted wave peaks advance, and the time to reach peak energy, stress, and strain decreases. This trend is consistent across all tested temperatures and pore ratios. Both lower temperatures and higher pore ratios lead to increased specimen strength and a marked shortening of the plastic plateau stage in the stress–strain curves. Based on the experimental results and the effective stress principle for saturated soils, a damage-enhanced constitutive model was developed within the Zhu-Wang-Tang constitutive framework by incorporating a wave-impedance term. This term links microstructural changes (ice content and cementation) to macroscopic strength, effectively characterizing the coupled effects of strain rate, temperature, and pore ratio. The model predictions show good agreement with the experimental data, providing a theoretical basis for the dynamic analysis of frozen soil engineering.
ABSTRACT The growing volume of agricultural waste and its improper management has led to severe environmental problems, particularly the one arising because of the open-field burning of crop residue. Agro-waste-based natural fibers have attracted significant interest as sustainable additives for bituminous mix applications. The use of natural fibers can effectively minimize the binder drainage, paricularly in gap- and open-graded bituminous mixes. Their wider use, however, has been limited by inherent limitations, including hydrophilic properties and weak interfacial bonding with the bituminous binder, particularly in the presence of moisture. As a remedial measure to the aforementioned challenges, modifying the surfaces of natural fibers has emerged as a promising approach. This study provides a systematic review of popular chemical and physical treatment methods for natural fibers and their application in bituminous mixes. The review study categorizes major treatment methods, such as chemical treatments (alkali, silane, permanganate, and acetylation) and physical treatments (plasma, corona, ultraviolet irradiation, and steam explosion), and then systematically compares their advantages, disadvantages, and suitability in bituminous mixes. Moreover, the behavior of bituminous mixes reinforced with chemically treated natural fibers is critically discussed in terms of the main performance factors, including rutting, fatigue behavior, moisture damage, and low-temperature cracking. Also, the factors related to optimizing fiber dosage, cost, and sustainability are discussed. The findings emphasize the possibility of chemically treated natural fibers to improve the functioning and longevity of bituminous mixes and encourage the use of agro-waste derived natural fibers. Lastly, critical conclusions are drawn, and future research directions are identified to enable the successful implementation of natural fibers in bituminous road construction works.
ABSTRACT Water scarcity impedes access to safe drinking water for billions and hampers global development initiatives. Conservation and reuse of potable water in concrete or mortar production is of great interest to fostering sustainable resource management in the construction industry. This research study sought to evaluate the comprehensive superiority of using alternative water sources in the construction industry. The use of treated wastewater at secondary and tertiary treatment levels for preparing cement mortar was evaluated. To simulate worst-case scenarios, treated wastewater was utilized in both mixing and curing. Municipal- and industrial-treated wastewater were selected to evaluate the fresh, mechanical and microstructural properties of cement mortar. Rheological behavior was assessed to investigate the flow properties of tap water and treated wastewater. Toxicity characteristic leaching procedure (TCLP) test was conducted on 90-d cured samples to examine leaching potential. The test results revealed variations in yield stress and plastic viscosity with change in water quality. Industrial-treated wastewater increased the initial and final setting times by 45 and 70 min, respectively. Secondary-treated wastewater used for both mixing and curing showed a 13 % reduction in compressive strength and a 21 % reduction in flexural strength at 28 d compared to the control mix. Microstructural analyses (scanning electron microscope, X-ray diffraction, and Fourier transform infrared spectroscopy) indicated that treated wastewater had no significant adverse effects on concrete or mortar properties. Furthermore, TCLP results confirmed the absence of heavy metals even after longer curing periods. The findings showed that secondary-treated wastewater can be safely used for concrete production with acceptable strength performance and regulatory compliance. This confirms its suitability as a sustainable alternative to potable water.
ABSTRACT Real-time sensing of the evolution of multi-source parameters within pavement structures is essential for effective construction, performance evaluation, and maintenance. This study develops a wireless smart aggregate (SA) sensor for real-time multiparameter monitoring within pavement mixtures. A strain transfer model for the sensing layer–encapsulation–medium system was derived, validated via finite element simulations, and applied to optimize the encapsulation design. The SA integrates strain gauges, accelerometers, magnetometers, and thermometers to capture stress, acceleration, rotation, and temperature simultaneously. Laboratory calibration verified that the sensor achieves high measurement accuracy, and gyratory compaction tests in cement-stabilized macadam demonstrated the sensor’s capability to track compaction-induced mechanical and kinematic changes. To mitigate material heterogeneity effects, a volatility index (Vt) combining stress and rotation variations was introduced, showing a strong correlation with compaction evolution (Pearson correlation coefficient ≥ 0.89). The results confirm the SA’s potential for multi-source in situ monitoring, offering an effective tool for pavement health assessment and intelligent construction control.
ABSTRACT Aluminum alloys, in particular 6xxx series alloys, find vast applications in the automobile sector, because of the intense need for light-weight materials to increase fuel efficiency. Gas metal arc welding is an extensively accepted joining technique for fabricating these aluminum alloys owing to the high productivity. But aluminum alloy joints are extremely vulnerable to salt spray corrosion because welding is associated with the breakdown and dissolution of intermetallics, and it is severe in case of high energy consumption processes. Therefore, low energy consumption welding processes like cold metal transfer (CMT) and pulsed CMT (PCMT) were employed. A salt spray corrosion test was employed to evaluate the corrosion properties of these welded joints. Salt spray testing revealed that corrosion attack was most severe in the fusion zone in contrast to the heat affected zone and base metal. Further, to enhance the corrosion resistance of the welded joints, micro-arc oxidation (MAO) coating technique was employed. From the electrochemical impedance spectroscopy (EIS) test data, it is confirmed that the absence of inductive characteristics in the coated specimens indicates the absence of localized corrosion, in contrast to the uncoated sample. The results show that MAO coatings exhibited higher corrosion resistance (i.e., ∼99 % improvement for CMT and PCMT joints) than the uncoated samples because of the formation of a thick oxide layer by a series of electrical discharges that can greatly resist corrosion.
ABSTRACT The properties of high-strength mortar (HSM) incorporating supplementary cementitious materials (SCMs) and various levels of steel fiber (TF) were studied. The measured properties include fresh properties, compressive and flexural strength, durability, and microstructure. The selected SCMs are calcined kaolin clay, limestone (LS), and quartz (QS) powder. The test results showed that the combination of clay and TF would reduce the workability and air content of fresh HSM mixtures. TF addition can boost the mechanical performances of HSM blended with SCMs. Moreover, TF addition improved the compressive strength of QS-HSM to a greater extent than that of LS-HSM. In contrast, the combination of TF, clay, and LS is conducive to increasing the bending strength of LS-HSM compared with those of QS-specimens at all TF levels. The shrinkage of HSM under different curing humidity can be alleviated by an appropriate TF dosage. In addition, the durability of HSM incorporating clay, LS, and TF is superior to that of QS-HSM. This HSM is designed with high-volume SCMs (40 % cement replacement) and low TF content (0–0.8 % volume fraction), retaining the core durable characteristics of traditional HSM with reduced carbon emissions and production costs.
ABSTRACT To enhance cotton/viscose fiber recognition accuracy, we propose a data classification-based digital analysis method. A set of image preprocessing algorithm procedures has been developed to perform grayscale, denoising, and binarization on the image. In addition, the fiber cross-sectional image is marked, and the outer contour image of the fiber cross-section is obtained by using the edge detection algorithm, and finally the feature parameters are extracted. By training and analyzing the extracted fiber cross-section characteristic parameters, three recognition algorithms including K-nearest neighbor, backpropagation neural network, and random forest–decision tree are used to identify the fiber category. The experimental results show that the recognition rate of the random forest–decision tree algorithm is the highest, and the recognition accuracy is up to 97.5 %.
ABSTRACT The previous freeze-thaw damage model for macroporous recycled concrete (MRC) neglected the pore structure damage that can reduce permeability. Furthermore, macro–mesoscopic factors, such as the old paste on recycled aggregates (RA), were not adequately considered. To address the aforementioned issues and establish a macro–meso coupled freeze-thaw damage model for MRC, freeze-thaw tests were conducted on saturated MRC and its meso-components, namely paste, interface transition zone (ITZ), and RA, in an air environment. The compressive strength, shear strength, indentation elastic modulus, ITZ thickness, old paste exfoliation rate, pore size distribution parameters, and average pore size of the paste, ITZ, RA, and MRC were measured. The test results indicated that, in an air environment, the freeze-thaw damage rates of the paste, ITZ, RA, and MRC were relatively low. Initially, their performance improved before gradually declining. When the paste compressive strength reached 62.5 MPa, the central region of the ITZ specimen exhibited earlier damage compared to the surface region. A novel method for quantifying the damage degree of MRC was developed by integrating changes in both macrolevel mechanical properties and mesolevel pore structure. The damage degree of the MRC was regressed by incorporating the damage degrees of the paste, ITZ, and RA, as well as the filling ratio. The damage degree of the MRC under any number of freeze-thaw cycles can be accurately predicted using the filling ratio of paste and the initial meso parameters of the paste, ITZ, and RA, with a calculation error ranging from 0.004 to 0.080.
ABSTRACT Reliable rock mechanics parameters are crucial for scientifically revealing the mechanical behavior of complex geological tunnels, enabling rational design, and ensuring construction safety. Through 25 groups of orthogonal finite element calculations on tunnel models and network sample construction, a novel swarm intelligence algorithm—sparrow search algorithm (SSA)—was introduced to optimize the weights and thresholds of a classical back-propagation (BP) neural network. This led to the development of an SSA-BP intelligent inversion model for rock mechanics parameters. Results demonstrate that the SSA-BP model significantly outperforms the traditional BP neural network in prediction accuracy and reliability. It exhibits stronger generalization and robustness, particularly for complex datasets, effectively addressing the BP network’s tendency to converge to local minima. The model was applied to invert mechanical parameters of surrounding rock in a complex mudstone-shale interbedded tunnel on Hubei’s Anlai Expressway. Using deformation monitoring data, an optimal exponential fitting model was established to analyze the nonlinear deformation characteristics of tunnel surrounding rock, accompanied by recommended engineering measures. Forward calculations via a three-dimensional mechanical model, using parameters inverted by SSA-BP, showed that the vault settlement error of typical cross-sections relative to field measurements fell within 1.41–5.29 %. Ground settlement and inverted arch uplift also matched well with field data, validating the model’s rationality and reliability. This research enriches the application of intelligent inversion methods for complex rock mechanics parameters, offering both theoretical insights and practical engineering value.
ABSTRACT The application of high-viscosity modifiers in asphalt modification is receiving increasing attention for facilitating the development of drainage pavements. However, compatibility issues and phase separation between modifiers and asphalt remain major challenges that restrict large-scale field applications, with the deterioration of rheological properties being a direct manifestation of insufficient compatibility. In this study, a high-viscosity asphalt modifier based on a styrene–butadiene–styrene block copolymer/terpene resin blend (SBS/terpene resin blend [STRB]) was prepared via direct injection using a polymer blending method. The chemical characteristics of STRB and its compatibility mechanism with asphalt were investigated at multiple scales through solubility parameter analysis, Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), surface energy analysis, and fluorescence microscopy, and compared with the two conventional modifiers, TAFPACK-Super (TPS) and Tianyi (TY). The modification effects were evaluated through comprehensive performance tests, including dynamic shear rheometer (DSR) testing, on STRB-modified high-viscosity asphalt (HVA-B), TPS-modified HVA (HVA-S), and TY-modified HVA (HVA-Y). The results demonstrate that, compared with the two conventional modifiers, STRB contains more oxygen-containing functional groups, exhibits higher polarity, and possesses superior compatibility with asphalt. This enhanced compatibility enables STRB to disperse more uniformly in asphalt with a smaller average particle size, thereby significantly improving its rheological properties. Consequently, the mechanical properties and high-temperature rutting resistance of HVA-B are markedly enhanced compared to HVA-S and HVA-Y. This study provides theoretical insights and technical references for the development of high-compatibility asphalt modifiers and has significant implications for advancing the engineering application of drainage pavements.
ABSTRACT Reinforced thermoplastic pipes (RTPs) are increasingly used in onshore and offshore oil and gas production industries owing to their lightweight and resistance to corrosion and pressure. Accurate estimation of pipe stiffness (PS) is crucial in onshore engineering. Further research is needed to define the properties of the parameters affecting stiffness. This study investigates the estimation of the stiffness of RTPs under transverse loads, incorporating analytical, numerical, and experimental research methodologies. Additionally, the influence of winding angle, thickness of the reinforced layer, and thicknesses of the liner and cover on the stiffness of the RTP is evaluated and discussed. To achieve this objective, a detailed analysis of composite pipes with external diameters of 90, 200, and 323 mm and wall thicknesses of 14.2, 16.6, and 13.6 mm, respectively, was conducted. These analyses involved the implementation of multiple diameter verification procedures to ensure the accuracy and reliability of the results. In analytical and numerical studies, a layered composite modeling approach has been adopted for RTPs. Consequently, isotropic and anisotropic material properties have been defined for each layer. The studies show that the thickness and winding angle of composite tapes significantly contribute to the stiffness of the pipe, whereas the thickness of the liner and cover is less effective. Additionally, increasing the winding angle to 90° significantly improved the PS. The findings provide a reliable and efficient method for calculating the stiffness of pipes under transverse loading. This method can be utilized during the design stage to simplify the design process for RTPs. A comprehensive evaluation of PS using a layered modeling approach enables researchers to make informed decisions regarding the use of thermoplastic composite tapes for piping applications. This approach can also be used to maximize pipe performance under various stiffness scenarios.
ABSTRACT Portland cement concrete pavements (PCCPs) are widely used for their durability, long service life, and ability to withstand heavy traffic. To ensure performance and reduce maintenance costs, regular evaluation of joint load transfer (LT) capacity is essential. Traditional destructive tests (DTs) provide direct LT data but are localized, time-consuming, costly, and damaging to the pavement. In contrast, nondestructive tests (NDTs) allow broader and non-invasive evaluation, though they require careful interpretation. NDT methods can be divided into discrete and continuous testing devices. Discrete devices, such as the Benkelman beam deflectometer and falling weight deflectometer, stop to apply forces and measure deflections at selected locations. Continuous devices, including the rolling dynamic deflectometer (RDD), rolling weight deflectometer (RWD), and traffic speed deflectometer (TSD), measure pavement deflections while moving at traffic speeds. These continuous systems offer significant advantages by enabling large-scale, traffic speed assessment; however, they generate vast datasets that pose challenges in processing, calibration, and test condition sensitivity. Recent advancements are improving the accuracy and efficiency of LT assessment. Modern high-speed deflectometers integrate advanced technologies such as Doppler velocity sensors for real-time monitoring, ground-penetrating radar for subsurface characterization, traffic-induced excitation as an alternative to impulse loading, and machine learning algorithms for automated data processing. Further, artificial intelligence combined with geographic information systems enhances pavement management systems, enabling smarter decision-making and cost-effective maintenance planning. Future developments in LT evaluation are expected to focus on refining RDD, RWD, and TSD technologies, improving calibration protocols, integrating multi-sensor data, and establishing standardized procedures. Special attention should also be given to applying these advanced methods to precast concrete pavements, where joint performance plays a critical role in service life. Overall, emerging NDT solutions offer great potential for more reliable, efficient, and sustainable pavement monitoring.
ABSTRACT Composite insulators are crucial in power systems, but their long-term stability under complex operating conditions still needs improvement. Nano-SiO2 has potential as a reinforcing filler. This study employs the molecular dynamics simulation method to construct a model of a nano-SiO2/methyl vinyl silicone rubber (MVSR) composite and systematically studies its microstructure evolution under the action of thermal, electrical, and mechanical stress. The results showed that the addition of nano-SiO2 significantly improved the microstructure stability of the nano-SiO2/MVSR composite, specifically manifested in more stable bond lengths, bond angles, and mechanical property parameters. When the electric field intensity reached 350 kV/mm, the total energy of the nano-SiO2/MVSR composite decreased by only 23.5 %, which was significantly lower than the 57.3 % decrease observed in pure MVSR, demonstrating its energy stability under the electric field. In addition, the simulations revealed that both composites exhibit a high dynamic modulus in the GPa range, characteristic of a high-strain-rate response. Under mechanical stress, the variation range of the mechanical modulus of the nano-SiO2/MVSR composite was also smaller than that of pure MVSR, indicating superior mechanical stability. These simulation results suggest that introducing nano-SiO2 can enhance the structural stability of the MVSR matrix at the microscopic level to cope with external stimuli such as heat, electricity, and mechanical stress. These quantitative findings are corroborated by direct visual evidence from simulation snapshots, which clearly illustrate the conformational changes of polymer chains under different stresses, ultimately providing a microscopic basis for improving the long-term service reliability of composite insulators. This study provides atomic-scale insights into the mechanism of nanofillers in polymer insulation materials and provides theoretical references for the design of high-performance composite insulation materials.
The slump flow test is typically used to determine the flowability of self-compacting ultra-high-performance concrete (SCUHPC). The volume of a typical concrete slump flow cone is approximately 5.5 L, meaning it consumes a significant amount of material. To reduce the quantity of the material used in slump flow tests and improve measurement efficiency, this study proposes using a small slump flow cone with a volume of 0.29 L to determine the flowability of SCUHPC and self-compacting mortar (SCM). In this research, the flowability of 20 mixtures was compared using a concrete slump flow cone and the proposed small slump flow cone. The slump flow diameter ratios of the two cones were also theoretically compared, and the theoretical and experimental results were strongly correlated. Consequently, the proposed small slump flow cone could be used to effectively evaluate the flowability of SCUHPC and SCM, establishing a correlation between code-recommended slump flow diameters.
After the publication of this article, a concern was received about its peer review process. In the course of our investigation, we found that one of the peer reviewers may have compromised the review process. We notified the corresponding author, as the representative of all authors, that we were returning the paper to review. After new reviews were obtained, we sent them first to the corresponding author, and then to all authors, and requested the paper be updated in an erratum per the new review comments. We did not receive responses from the authors. We add this Expression of Concern to the paper because we do believe there are credible concerns about the reliability of the article. Depending on response by the authors, the status of this article may be updated. Citation of Original Article: S. M. Vahabi, M. S. Zafarghandi, and M. Bahreinipour, “Studies on the Photon Interaction Parameters for Some Vitamins Using Monte Carlo Simulation,” Journal of Testing and Evaluation 47, no. 6 (November/December 2019): 4513–4522. https://doi.org/10.1520/JTE20170755
