Temperature is a critical influence factor for measurement accuracy of asphalt mixture (AM) density based on non-destructive testing methods, which will change the dielectric constant of AM and consequently affect the measured results. Previous studies have proposed several methods to eliminate temperature-induced errors based on the observed relationship between dielectric constants of AM and temperature at medium and low temperatures. However, these methods may be ineffective at high temperature conditions, such as during hot summer and compaction process. This paper developed a novel measurement method combining laboratory tests and numerical simulations based on parallel-plate capacitance sensor, which could accurately obtain the dielectric constants of AM and its raw materials from -10 degrees C to 150 degrees C. Subsequently, a temperature-dependent dielectric mixing model was established for removing the effect of temperature. Finally, this model was compared with traditional dielectric mixing model under varying temperature conditions through laboratory tests. The results indicate that within the wide temperature range, the dielectric constants of asphalt binder, aggregate, and AM exhibited strong linear relationships with temperature, with the R-2 values of their fitting curves all exceeding 0.97. Additionally, the dielectric constant of aggregates dominated that of AM due to high volumetric proportion. It was also found that the proposed temperature-dependent dielectric mixing model exhibited lower temperature interference and higher accuracy than traditional model in laboratory tests, whose errors were 2.962 % and 3.702 % in full temperature range and were 2.998 % and 9.365 % in high temperature condition (> 80 degrees C), respectively. These findings offer a widely applicable mechanism and model for eliminating the influence of temperature and further improving measured accuracy in AM density measurements within a wide temperature range, especially for high temperature conditions.

This study presents a comprehensive multi-scale evaluation of the frost resistance of cement-stabilized macadam (CSM) modified with multiple admixtures. A frost-resistant variant (FCSM), enhanced with expansive agents, latex powder, superabsorbent polymers, and polypropylene fibers, was systematically compared to conventional CSM. The evaluation incorporated mechanical tests, capacitive sensing, ultrasonic velocity analysis, acoustic emission monitoring, and microstructural investigations using Scanning Electron Microscopy (SEM) and Mercury Intrusion Porosimetry (MIP). Under freeze-thaw cycling, the FCSM demonstrated significantly greater strength retention, mass stability, and internal compactness. Capacitance degradation exhibited strong correlation with both ultrasonic velocity reductions and mechanical performance loss, confirming the reliability and sensitivity of capacitive sensing for frost damage detection. Acoustic emission results further indicated suppressed crack propagation in FCSM during compression. Microstructural analysis revealed that the admixtures effectively reduced detrimental porosity and improved matrix integrity. These findings underscore the effectiveness of capacitive sensing as a quantitative, non-destructive evaluation method, and highlight the importance of integrating mechanical, physical, and microstructural parameters for the robust assessment of frost durability in cement-bound road materials.

Cracking has always been a critical problem for concrete pavement, which would result in performance degradation and even structural failure. Conventional theoretical models and methods, such as the cohesive zone model and extended finite element method, have been widely employed for concrete cracking simulation. However, these approaches still suffer from mesh dependency and numerical complexity. To address these issues, this paper introduced a phase field regularized cohesive zone model (PF-CZM) to simulate the macroscale and mesoscale fracture behavior of concrete. This model was numerically implemented by the finite element method and validated through a traditional example. The sensitivity of calculations to mesh and internal length scale parameter b was discussed. Subsequently, typical Mode I and mixed-mode fracture tests were simulated to investigate the feasibility of PF-CZM in modeling quasibrittle fracture of concrete. The results indicated that simulated crack width was determined by internal length scale parameter b, but the other calculated results of PF-CZM exhibited high independence from element type, mesh size h, and internal length scale parameter b when b/h >= 5. Further, the simulation results of PF-CZM were consistent with experimental data, which proved the applicability and effectiveness of PF-CZM in modeling the fracture behavior of concrete at the multiscale. Based on these calculations, the mesoscopic process of damage and crack evolution in concrete were demonstrated. It was also observed that interfacial transition zones were the primary region where cracking and damage were induced due to their inadequate tensile strength and fracture energy. Additionally, different shapes and sizes of aggregates in the crack propagation region led to various deflections during crack propagation and eventually resulted in the presence of diverse crack paths.

The two-dimensional (2D) dynamic coplanar capacitance imaging technologies have high imaging accuracy and detection efficiency in identifying internal damages in asphalt layers. However, these technologies are difficult to provide a three-dimensional (3D) visual damage distribution, and the reconstructed 2D damage images only refer to the 2D projection of the 3D damage. Thus, a novel direct 3D dynamic coplanar capacitance imaging method is proposed to image the 3D damage distribution in asphalt layers. Initially, the 3D dynamic sensitive field distribution is constructed. Secondly, the normalized coplanar capacitance of the measured electrode pairs is discussed. Finally, the 3D internal damages in asphalt layers are reconstructed and analyzed. It is concluded that the imaging accuracy of the middle-upper static and dynamic sensitive layers, which are closer to the sensor, is higher than that of their lower layers. The approximate spatial location of internal damages in asphalt layers is determined according to the maximum coplanar capacitance of adjacent diagonal electrode pairs in different scanning steps. Square damage exhibits the highest 3D imaging accuracy with an error of 13.25 %, followed by circular damage and triangular damage. As the depth of asphalt layers increases, the 2D slice imaging accuracy for circular and square damages improves, whereas the accuracy for triangular damage decreases. The findings of this investigation have the potential to assist engineers in the 3D visual identification of internal damages in asphalt layers combined with the ground-penetrating radar (GPR) method.

Coplanar array capacitance imaging technology (CACT) is a non-destructive testing (NDT) method with rapid visual recognition. However, there is little research on its application for identifying internal distress in asphalt materials. This study proposes a CACT using a novel coplanar array capacitance sensor (CACS) to identify internal distress in asphalt materials. Firstly, the sensitivity field distributions of electrode pairs and CACS are established, and the optimal sensitivity layer is determined. In addition, the normalized capacitance of electrode pairs and the contribution rate required for distress imaging are analyzed. Finally, the distress images of asphalt materials are reconstructed. The relative depth, shape feature, and type of internal distress are determined. It is found that Layer 14 is the optimal sensitivity layer. Circular distress exhibits the highest normalized capacitance values, followed by square distress and triangle distress. The opposing electrode pairs demonstrate the greatest contribution to distress imaging. The redder the image, the deeper the distress, demonstrating that CACT can determine the relative depth of internal distress. The square distress image has the highest accuracy, followed by circular distress and triangle distress. The reconstructed images show that moisture distress in asphalt mixtures is reconstructed with the highest accuracy, followed by moisture distress in asphalt mastics, void distress in asphalt mastics, and void distress in asphalt mixtures.

Self-sensing Cement-based Materials (SCMs) have garnered significant attention as a promising smart construction material. While resistance-based self-sensing has been extensively applied in civil infrastructure, capacitance-based self-sensing, an emerging approach, presents several advantages in terms of mix design, preparation processes, cost, workability, and self-sensing performance, particularly with regard to reversibility, linearity, and sensitivity. This method has seen substantial progress in recent years. However, the summary of the capacitance-based self-sensing in terms of cement-based materials, as well as a comparative analysis between capacitance-based and resistance-based self-sensing of cement-based materials, is lacking. Thus, this research aims to provide a comprehensive review of SCMs utilizing both resistance-based and capacitance-based self-sensing methods. The review first presents the conductive admixtures, measurement, performance, and sensitivity of resistance-based SCMs. Subsequently, it summarizes the measurement, performance, and sensitivity of capacitance-based SCMs. A comparative analysis is also provided, assessing the matrix composition, electrical measurement, performance, and sensitivity of both self-sensing methods. Finally, the current applications of resistance-based and capacitance-based self-sensing are briefly discussed. This review seeks to provide insights into the development and future directions of self-sensing SCMs, emphasizing the strengths and limitations of each method.

Asphalt concrete (AC) attains the ideal density during compaction is critical to ensure construction quality and service performance of asphalt pavement. Therefore, it is imperative to develop a novel instrument and methodology capable of continuously evaluating AC density during the compaction stage. The interdigital coplanar capacitive sensor (ICCS) has exhibited potential for AC density measurement. However, its application during compaction is hindered by the negative impact of water sprayed by roller, which distorts the capacitance data. This study proposes an innovative approach to remove the surface moisture effect from capacitance data. Initially, the method for removing surface moisture effect from capacitance data was proposed, which consists of the dielectric mixing model, generalized partial capacitance model (GPC) model, and conformal mapping technique (CMT). Subsequently, the feasibility of proposed removal method was verified by numerical simulations and experimental tests for various scenarios. Finally, the predicted accuracy of AC density by ICCS was discussed before and after removing the fluence of surface moisture. The findings demonstrated that the GPC model, in conjunction with the CMT and dielectric mixing model, effectively calculated the capacitance value for any multi-layer structure, thereby considering the influence of surface moisture. Furthermore, the proposed theoretical approach effectively removed the surface moisture impact from both simulated and experimental capacitance data, enabling the accurate determination of the dielectric constant and the dry AC density. The measured error of the ICCS with water reduced from 10.68 % to 6.87 % after removing the its effect, which are close to that without water.

Bitumen emulsion-based cold in-place recycling has been widely used all around the world due to its superior environmental benefits. During the mixing process of cold-mix asphalt (CMA) mixtures, extra water is introduced to enhance workability and ensure uniform coating. The limited understanding of moisture migration in the curing process has restricted the broader application of this technology in pavement maintenance. Therefore, this paper aims to develop a novel moisture migration characterization method for CMA mixtures over curing based on capacitive measurement. A semi-sealed laboratory curing method was employed to simulate practical construction and the moisture migration in the depth direction and radial direction of CMA mixtures was analyzed by a self-designed three-layer circular capacitance sensor (CCS) during the curing period. It is found that the capacitance values of asphalt specimens measured by CCS show a good "U" curve trend. Two parameters (Cs and epsilon m) for evaluating moisture migration in depth direction were proposed. The variation of Cs and epsilon m demonstrated the gradient moisture distribution in the depth direction during the first stage of curing. And the rate of moisture consumption was uniform during the second stage. All three layers of CMA-13 exhibited a gradient distribution in the radial direction without side waterproof layer, while the imaging within the circular cross-section is uniform in semi-sealed curing condition, with no gradient distribution. These findings suggest that electrical capacitance method could provide a tool for characterizing and visualizing the moisture migration in CMA mixture non-destructively.

As the civil aviation industry advances, higher requirements are put forward for the performance of airport cement pavement. The frequent takeoffs and landing of aircraft will cause continuous and large impact load on airport pavement, and combined with long-term natural factors, there will be various structural diseases. These diseases not only affect the service life of pavement, navigation safety. To enhance the impact resistance and durability of concrete, the incorporation of fiber has proven an effective approach, and basalt fibers (BF) and polypropylene fibers (PF) are selected as reinforcement materials in this study. Firstly, the effects of single BF and hybrid BF and PF on the mechanical properties of concrete were explored through compressive and flexural tests considering fiber volume contents (0.1%, 0.15, 0.2%) and lengths (6 mm, 9 mm, 12 mm). Then, the drop hammer impact test method, recommended by ACI committee 544, was refined to evaluate the impact resistance toughness of concrete. Finally, the numerical simulation of the impact test was established to explain the concrete impact damage mechanism. The results show that under less than 0.15% volume content, the single BF can effectively enhance the compressive strength, flexural strength, and impact resistance of concrete. Compared with the single BF, the combination of 12 mm BF and 6 mm PF yields favorable results, encompassing improved mechanical properties and impact resistance, thus exhibiting a positive hybrid effect. The increase in PF length (9 mm) leads to a reduction in compressive strength but an improvement in flexural strength and impact resistance. The simulation of the impact test is constructed and the accuracy is confirmed by comparing the impact times and failure patterns of the specimens. The tensile damage of plain concrete is much greater than the compressive damage, and the cracks will spread rapidly from the upper surface to the lower surface. Compared with plain concrete, the crack width of hybrid fiber reinforced concrete is larger, but the damage degree is more uniform. Fiber can absorb a large amount of impact energy, reduce the emergence of micro-cracks, and improve the impact toughness and toughness coefficient of concrete.

Inverted asphalt pavement is an innovative pavement structure where the unbound aggregate base (UAB) is sandwiched between the asphalt concrete (AC) layer and cement-treated base (CTB). This structure has the potential to improve rutting resistance of pavement and prevent propagation of reflective cracks. However, its implementation has been limited due to the lack of experience. In addition, the braking condition of the vehicle applied on the pavement surface plays a significant role on the pavement responses, thereby affecting its service life. This study conducted a simulation using finite layer method to investigate the effects of heavy truck braking on inverted asphalt pavement considering vehicle dynamics. The results indicated that truck braking would generate a significant horizontal load and change the magnitude of the vertical load at each axle. Furthermore, the impact of vertical load on mechanical responses would gradually enhance and eventually dominate with increasing pavement depth. It was also observed that when the truck was treated as a whole, vehicle braking would result in a 3.2% to 10.2% increase in critical responses, but cause a 0.023% and 31.7% decrease in predicted rutting and life respectively. Among three truck axles, the effect of driving axle was close to that of the entire truck, but the effect of other axles was greater or lower than that of the entire truck. A comparison of three inverted pavements found that a thick AC layer could effectively reduce the critical responses and extend the pavement life. Furthermore, a thick CTB layer could also achieve most of the above purposes due to higher integral stiffness of pavement.

Pavement cracks are a kind of common distress in road service time, and their length measurement is critical for pavement maintenance. The current automatic method of crack length measurement uses segmentation algorithms to obtain crack curves, which is time-consuming and complex. In this study, an effective method of crack length measurement was proposed and validated. The method consists of a detection module based on an object detection algorithm and a length calculation module. To increase the speed and accuracy of crack detection, an improved pavement crack detection algorithm BiFPN-enhanced YOLO V5 (YOLO V5-BiFPN) based on you look only once version 5 (YOLO V5) and bidirectional feature pyramid network (BiFPN) is proposed, and gamma correction was utilized to process pavement images. YOLO V5-BiFPN was tested in a real pavement image data set and achieved remarkable performance. In the length calculation module, the diagonal length of the crack bounding box output by the object detection algorithm can be defined as the crack length. To validate the measurement method, the true value of crack length was obtained from the segmentation data set by skeletonization. The error between the calculation result of the proposed method and the real value is 3.4%, and the average processing time of each image is 14.2 ms. The developed method addresses the problem of considerable time and financial cost associated with the existing crack length measurement methods.

Hydrogen production by photosynthetic hybrid systems (PBSs) offers a promising avenue for renewable energy. However, the light-harvesting efficiency of PBSs remains constrained due to unclear intracellular kinetic factors. Here, we present an operando elucidation of the sluggish light-harvesting behavior for existing PBSs and strategies to circumvent them. By quantifying the spectral shift in the structural color scattering of individual PBSs during the photosynthetic process, we observe the accumulation of product hydrogen bubbles on their outer membrane. These bubbles act as a sunshade and inhibit light absorption. This phenomenon elucidates the intrinsic constraints on the light-harvesting efficiency of PBSs. The introduction of a tension eliminator into the PBSs effectively improves the bubble sunshade effect and results in a 4.5-fold increase in the light-harvesting efficiency. This work provides valuable insights into the dynamics of transmembrane transport gas products and holds the potential to inspire innovative designs for improving the light-harvesting efficiency of PBSs.

The voids beneath cement concrete slabs are a major invisible disease, resulting in a rapid decrease in service performance in the composite pavement. Accurate voids prediction is essential for the extensive application and long-term service of composite pavement. This research provides a FEM-ANN (Finite Element Modelling-Artificial Neural Network) method to predict the voids beneath concrete slabs. These ANN models include the original back propagation (BP), the particle swarm optimisation (PSO) BP model, the genetic algorithm (GA) BP model, and the whale optimisation algorithm (WOA) BP model. The voids FEM model is established and validated by the measured data in the field, and the relative error of measured and simulated results is within 4%. The cross-validation results show that the WOA-BP model has the best prediction performance, with the highest score of 8, which refers to the overall score of the mean value and variance of these evaluation indices. Therefore, this FEM-ANN framework is an efficient method for estimating the voids beneath concrete slabs. Furthermore, it is discovered that the base modulus with the highest contribution degree of 20.34% is the most dominant factor in predicting the voids output.

In recent years, steel slag has gained widespread utilization in pavement construction as a green, low-carbon building material. In this study steel slag aggregate (SSA) was used as a partial replacement for basalt aggregate in porous asphalt mixture. To gain an in-depth understanding of the microscopic cracking characteristics of steel slag aggregate porous asphalt concrete (SSA-PAC), laboratory three-point bending tests and numerical simulations were conducted. Based on CT scanning images, 3D models of SSA and basalt aggregates within particle size ranges of 4.75-9.5 mm and 9.5-13.2 mm were generated. These characteristic aggregates were then randomly placed to establish a three-dimensional model using PFC3D (Particle Flow Code in 3 Dimensions). The micro-contact parameters were derived through the conversion and calibration of macro and micro-mechanical parameters. The results of the 3D virtual strength test exhibited a close resemblance to those obtained from laboratory semi-circular bending (SCB) tests. The displacement trend chart of the aggregates revealed three distinct stages: vertical displacement, vertical displacement combined with a small amount of lateral displacement, and lateral displacement. Cracking mainly occurred at the interface between the aggregates and asphalt mortar, which aligned with actual cracking scenarios. The cracking behavior of the asphalt mixture was characterized by evaluating the number of cracks, notch width, and coordination number throughout the loading process. Additionally, the influence of loading rates on cracking behavior was analyzed. The results of this study demonstrated that the established 3D model effectively revealed the cracking characteristics of SSA-PAC.

Civil engineering materials are the general terms for all types of materials used in civil engineering. The civil engineering materials for transportation infrastructure mainly include aggregates, inorganic and organic binders, mixtures, and steel, which are used highways, railways, bridges, tunnels, airports, etc. The mechanical and non-mechanical properties are fundamental for the materials and should meet the structural and functional requirements of the transportation infrastructure. This chapter introduces the fundamental properties of civil engineering materials. It starts by introducing the background and classifications. It then presents the mechanical properties of civil engineering materials, including different types of deformation, concept of stress and strain, strength, elasticity, plasticity, toughness, brittleness, stiffness, ductility, hardness, viscosity, and viscoelasticity. Then, it introduces the non-mechanical properties, including density, thermal properties, surface properties, water-related properties, durability and workability.

Metal nanomaterials can facilitate microbial extracellular electron transfer (EET) in the electrochemically active biofilm. However, the role of nanomaterials/bacteria interaction in this process is still unclear. Here, we reported the single-cell voltammetric imaging of Shewanella oneidensis MR-1 at the single-cell level to elucidate the metal-enhanced EET mechanism in vivo by the Fermi level-responsive graphene electrode. Quantified oxidation currents of ~20 fA were observed from single native cells and gold nanoparticle (AuNP)-coated cells in linear sweep voltammetry analysis. On the contrary, the oxidation potential was reduced by up to 100 mV after AuNP modification. It revealed the mechanism of AuNP-catalyzed direct EET decreasing the oxidation barrier between the outer membrane cytochromes and the electrode. Our method offered a promising strategy to understand the nanomaterials/bacteria interaction and guide the rational construction of EET-related microbial fuel cells.

Asphalt pavement has the potential ability of micro-crack self-healing during the rest periods, and temperature is the main influencing factor. In order to reveal the effect of changing temperature on the self-healing performance of asphalt micro-cracks by microwave heating, the crack models of asphalt binder consisting of three and four components were constructed and the molecular dynamics (MD) method was used to analyze the diffusion performance of the asphalt crack under the conditions of constant temperature and changing temperature. The mean square displacement was analyzed by the density change curve and the diffusion coefficient was obtained through determining the number of frames when the crack was self-healed. The results show that under the same temperature parameter simulation conditions, both the three and four component asphalt crack models healing efficiency of asphalt micro-cracks was the best under the constant high temperature of 373 K, followed by the changing temperature from 303 K to 373 K, and healing efficiency under the constant low temperature of 303 K was the lowest. The diffusion coefficient in the changing temperature is close to the diffusion coefficient at a constant high temperature, which is reasonably close to the self-healing efficiency of both. The improved simulation method can better describe the diffusion ability of asphalt cracks under the effect of temperature change, and provide a theoretical basis for microwave heating to promote the asphalt crack self-healing.

Cracking failure of cement-treated base（CTB） has always been the concern of highway constructors.Mesoscale cracking analysis is an important means to study the damage degradation mechanism,which is difficult to be characterized by experimental techniques alone.The objective of this paper is to develop a random aggregate modelling method to simulate the mesoscopic cracking of CTB material.A minimum rectangle area method was proposed to calculate the polygon aggregate size,which is closer to the sieving analysis than the average radius method.A buffer zone method was proposed to determine the distance between randomly generated polygon aggregates.Based on the proposed random algorithm,finite element method（FEM） was adopted to build the mesoscopic model of CTB including aggregate,mortar,interfacial transition zone（ITZ） and air voids.Laboratory tests were conducted to validate the numerical model.Then the sensitivity analyses were conducted to study the influencing factors on cracking behavior.The simulation results indicate that the higher aggregate content and the finer gradation lead to the increase of ITZ,thus reducing the cracking resistance of the CTB material.Low porosity content is able to significantly reduce the stress concentration and thus improves the cracking resistance.The research results of this paper could be used to guide the crack resistant design of CTB material.

To repair the voids and cracks in the Cement-stabilized macadam base (CSMB), a grouting material with lower elastic modulus, enhanced fluidity and slight expansion called Flexible Grouting Material (FGM) was developed using superfine cement, fly ash (FA), silica fume (SF), vinyl acetate-ethylene (VAE) copolymer emulsion, and polyvinyl alcohol (PVA) fiber. After that, the study optimized the proportion of FGM using Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in Multi-Objective Optimization (MOO). Firstly, to determine the component contents for subsequent experiments, the fluidity, setting time, compressive/flexural strength (1d + 3d), and ratio of compressive strength to flexural strength (C/F, 1d + 3d) were tested. Subsequently, the Response Surface Methodology-Box Behnken Design (RSM-BBD) was employed using VAE content, PVA content, and water-to-cement ratio (W/C) as factors. Additionally, the fluidity, setting time, compressive/flexural strength and C/F (28d), elastic modulus (28d), and drying shrinkage (28d) were considered as responses in RSM-BBD. The VAE content and W/C significantly influenced the response values, while the PVA content had a relatively minor influence. Finally, the NSGA-II was utilized to determine the optimal proportion of FGM. Compared to the control group, the optimal FGM demonstrated 16.53 % increase in fluidity, 37.86 % decrease in elastic modulus (28d), 47.98 % decrease in C/F (28d), 22.22 % reduction in drying shrinkage (28d), and 54.07 % increase in final setting time.

提出了一种基于目标检测与迭代阈值分割的道路标线分割算法.首先采用基于BiFormer改进的YOLOv5目标检测算法对道路标线区域进行快速定位与框选,然后运用快速迭代阈值分割对框选区域内的道路标线进行精细提取,最后对提取后的道路标线采用韦伯对比度进行人眼可视度评估.结果表明:该方法能够完成道路标线的快速准确提取,并实现对道路标线可视度的有效检测.