Intelligent Compaction Measurement Values (ICMV), widely used to evaluate construction quality in road engineering, are well-suited for continuous composite materials like asphalt. However, their accuracy is significantly compromised when applied to subgrade layers with discrete soil compositions. To address this issue, this paper proposes an evaluation framework adapted to the discrete structure of subgrades, based on the principles of granular dynamics and nonlinear modeling. The compaction status and degree of the subgrade layer are characterized by low-frequency and high-frequency wavelet coefficients, which serve as inputs to a shallow neural network. By adopting the multi-instance regression training method, the demand for the sampling quantity of the ring knife method by the model is significantly reduced. This model outputs a precise compaction degree directly, rather than relying on ICMV. Furthermore, through statistical correlation analysis, the study demonstrates the critical role of high-frequency components (previously often regarded as noise) in the assessment. Finally, the accuracy of the model in estimating the compaction degree is validated in two actual construction projects(R1 = 0.96, R2 = 0.87).

Unmanned rollers are typically equipped with satellite-based positioning systems for positional monitoring. However, satellite-based positioning systems may result in unmanned rollers driving out of the specified compaction areas during asphalt road construction, which affects the compaction quality and has potential safety hazards. Additionally, satellite-based positioning systems may encounter signal interference and cannot locate unmanned rollers. To solve this problem, a lateral positioning method for unmanned rollers is proposed to realize the positioning of unmanned rollers relative to asphalt road. First, we captured images from different perspectives and developed a dataset for asphalt road construction. Second, a method for boundary extraction of asphalt road is proposed to accurately locate pixels of asphalt road boundary. Subsequently, the lateral distances are measured by the designed lateral positioning methods. Finally, field validation experiments are conducted to evaluate the effectiveness of the proposed lateral positioning method. The results indicate that the method excels in extracting the asphalt road boundary. Furthermore, the proposed lateral positioning method shows excellent performance, with a mean relative error of 3.40% and a frequency of 6.25 Hz. The proposed lateral positioning method meets the performance requirements for lateral positioning in both accuracy and real-time in asphalt road construction for unmanned rollers.

The positioning for unmanned rollers is typically achieved by global positioning systems (GPS), but GPS easily suffers from signal blockage. In contrast, visual simultaneous localization and mapping (SLAM) provides a robust solution because it does not rely on satellite signals. However, dynamic features in construction environment have a negative influence on the positioning accuracy of unmanned rollers. To solve this problem, a highefficiency semantic segmentation model is constructed to eliminate dynamic features. Additionally, a static feature compensation mechanism is proposed to increase the number of effective features available for SLAM. To verify the positioning accuracy of the proposed SLAM, a specialized image dataset for roller construction environments with dynamic features is developed. Field validation experiments demonstrate that the proposed SLAM has a high positioning accuracy, with a mean relative error of 5.33 %. Compared with state-of-the-art SLAM methods, the proposed SLAM effectively reduces the positioning errors of unmanned rollers in dynamic construction environments, with the reduction of mean relative error at least 40.65 %. The proposed SLAM has been applied to monitor compaction counts of construction areas and has achieved commendable results.

3D printing is an excellent choice for manufacture of ceramic cores; however, the inter-layer structures in 3D printed sample leads to anisotropy in microstructure and properties. In this paper, silica-based ceramic cores were prepared by digital light processing, and metallic Si powder was employed as mineralizer to relieve the anisotropy. The metallic Si powder converts into SiO2 in oxidation reaction with significant volume expansion, which inhibits the shrinkage rate and partly fills the interlayer gaps of the ceramic core. Meanwhile, the metal Si powder leads to lower curing thickness in 3D printing, and thicken the interlayer gaps. Due to the cooperation of the oxidation reaction and decreased curing thickness by the metallic Si powder, the 0.4 wt.% of metallic Si powder reduced the anisotropy in structure and mechanical property due to the alleviated inter-layer structure and the interlayer and intralayer strengths show optimal values of 11.4 MPa and 17.2 MPa at room temperature, respectively. This work inspires new guidance to 3D printing of ceramic cores with uniform microstructure and excellent mechanical properties.

High-performance alumina-zirconia ceramics are generally sintered above 1500 degrees C and consume lots of energy; rapid sintering at low temperature is an ideal technique due to its economy and low-energy consumption. For the first time, flash sintering combined with phase transformation-assisted sintering techniques was reported in this work. The alumina-zirconia amorphous precursor powder was used to replace the final product powder for ceramic processing, and the one-step synthesis and rapid sintering densification of alumina-zirconia ceramics at low temperature of 849 degrees C for 60 s were realized. The effects of electric field on the low-temperature sintering of amorphous powders are summarized into three aspects. Firstly, the electric field reduces the viscosity of the amorphous powder and promotes liquid phase sintering. Secondly, the electric field promotes the phase transition of the amorphous powder from metastable to stable phase, and accelerates the atomic migration. Thirdly, Joule heating and defect generation in the flash sintering promotes more effective material transport. Overall, this work reduces the energy for calcination of amorphous powders, reduces the sintering temperature of ceramics, well hinders the grain coarsening and shows excellent mechanical properties.

Today, with the rapid development of unmanned driving, the development of hardware facilities for autonomous vehicles has become highly mature. This paper achieves the driverless function of the roller based on the visual slam algorithm. Firstly, the double steel wheel vibration roller is modified, and the roller is equipped with a Zed stereo camera, a Tx2 industrial control computer, and a STM32 drive board. The ROS system is used as the carrier to ultimately achieve unmanned driving of the roller. After the stereo camera collects the image information, it is transmitted to the industrial computer tx2 for processing, and the point cloud map is converted for output. At the same time, the tx2 and stm32 driver boards also transmit data, and a path is planned for the roller through trajectory planning, thereby achieving the autonomous driving function of the roller. The experimental results show that the unmanned roller based on visual slam can effectively locate in real time and realize the automatic driving of the roller.

As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.

Nanostructured amorphous composites are novel materials comprising nano-sized crystal phases embedded in an amorphous matrix, but often necessitating ultra-high-pressure molding for preparation. In this study, nanostructured amorphous ZrO2-Y2O3 composite ceramics were rapidly prepared for the first time using flash viscous flow sintering without the application of pressure. The relative density of the ZY ceramics increased with increasing current density, and ZY composite ceramics with relative density of 86%-97% were prepared by FS in a short time of 60 s under 700 V/cm with 0.16-0.56 A/cm2. Furthermore, the sintering activities in FS were compared between partially amorphous powder and crystalline powder. The ceramics prepared by flash sintering of partially amorphous powder show a much higher relative density than that from crystalline powder, suggesting that partially amorphous powder achieves easier densification than crystalline powder in flash sintering under given current density and power dissipation. This phenomenon was attributable to the differing sintering mechanisms between the two materials, i.e., solid-state sintering for crystalline powder and viscous flow sintering for partially amorphous powder.

To solve the problem of the precise position of the dumping points in autonomous excavation, a method of dumping point localization is proposed by using a pose estimation system of monocular vision markers in the excavator coordinate system. Firstly, a marker and a camera are used to establish a pose system prototype of the dumping point for the autonomous excavation. Then, based on the principle of pose estimation, a visual marker detection system is designed, and the position error analysis of pose estimation is proposed. Finally, the field test shows that the proposed localization method can precisely obtain the position of the dumping point.

é’ˆå¯¹æœºå™¨è§†è§‰ç³»ç»Ÿä¸­é€è§†æŠ•å½±å¯¼è‡´çš„é’¢ç®¡ã€æ¡¥æ¢æ‹‰ç´¢ç­‰åœ†æŸ±è¡¨é¢ç¼ºé™·æµ‹é‡ä¸å‡†çš„é—®é¢˜ï¼Œæå‡ºäº†ä¸€ç§åœ†æŸ±æ›²é¢é€è§†æŠ•å½±å¤±çœŸçš„å›¾åƒæ ¡æ­£æ–¹æ³•ã€‚è¯¥æ–¹æ³•åœ¨æå–åœ†æŸ±æˆåƒåŒºåŸŸã€æ˜Žç¡®æ¨ªå‘å’Œè½´å‘æ–¹å‘çš„åŸºç¡€ä¸Šï¼ŒåŸºäºŽåœ†æŸ±æ›²é¢çš„é€è§†æŠ•å½±ç‰¹æ€§ï¼Œå°†äº§ç”Ÿçš„å¤±çœŸåˆ†è§£ä¸ºè½´å‘å˜å½¢å’Œæ¨ªå‘å˜å½¢ï¼Œåˆ©ç”¨ç³»ç»Ÿæˆåƒå‚æ•°å’Œåœ†æŸ±åŠå¾„å»ºç«‹æ ¡æ­£å›¾åƒä¸ŽåŽŸå›¾åƒçš„åæ ‡å¯¹åº”å ³ç³»ï¼Œé€šè¿‡æœ€é‚»è¿‘æ’å€¼æ³•è¿›è¡Œåƒç´ æ˜ å°„å®žçŽ°é€è§†æŠ•å½±å¤±çœŸæ ¡æ­£ã€‚å®žéªŒç»“æžœè¡¨æ˜Žï¼Œè¯¥æ–¹æ³•å¯¹ä¸åŒç›´å¾„åœ†æŸ±çš„å›¾åƒå‡æœ‰è‰¯å¥½çš„æ ¡æ­£æ•ˆæžœï¼Œæ ¡æ­£åŽçš„å›¾åƒæ¶ˆé™¤äº†åœ†æŸ±æ›²é¢â€œè¿‘å¤§è¿œå°â€é€è§†å˜å½¢å’Œå€¾æ–œæŠ•å½±å˜å½¢ï¼›è¿›è¡Œæ£‹ç›˜æ ¼æ¨¡æ‹Ÿæ ¡æ­£æ—¶ï¼Œ6ä¸ªåœ†æŸ±æ£‹ç›˜æ ¼è¾¹é•¿çš„æµ‹é‡è¯¯å·®ä»Žæ ¡æ­£å‰æœ€é«˜çš„14.9%é™ä½Žè‡³æ ¡æ­£åŽçš„1.2%；进行划痕测量时，两种直径的原图像的最大误差分别为78.0%与61.8%ï¼Œç»æœ¬æ–‡æ–¹æ³•æ ¡æ­£åŽï¼Œä¸¤ç§ç›´å¾„çš„æœ€å¤§è¯¯å·®ä» ä¸º5.9%与5.5%ï¼Œæ ¡æ­£æ•ˆæžœæ˜¾è‘—ã€‚

Rollers, integral to road construction, are undergoing rapid advancements in unmanned functionality. To address the specific challenge of unmanned compaction within tunnels, we propose a vision-based odometry system for unmanned rollers. This system solves the problem of tunnel localization under conditions of low texture and high noise. We evaluate and compare the performance of various feature extraction and matching methods, followed by the application of random sample consensus (RANSAC) to eliminate false matches. Subsequently, Perspective-n-Points (PnP) was employed to establish a minimal-error analysis for pose estimation and trajectory analysis. The findings reveal that binary robust invariant scalable key points (BRISK) exhibits larger errors due to fewer correctly matched feature points, while scale invariant feature transform (SIFT) falls short of real-time requirements. Compared to Oriented FAST and Rotated BRIEF (ORB) and the direct method, the maximum relative error and the median error between the compaction trajectory estimated by speed-up robust features (SURF) and the actual trajectory were the smallest. Consequently, the unmanned rollers employing SURF + PnP improved the accuracy and robustness. This research contributes valuable insights to the development of autonomous road construction equipment, particularly in challenging tunnels.

为提高挖掘机自主作业的效率,针对一个完整的挖掘周期提出了一种时间近似最优挖掘轨迹生成框架.结合人工操作的经验进行分析,完成挖掘机的运动状态估计.利用三次样条曲线表示挖掘轨迹,并设定始末位置的速度和加速度,生成平滑的轨迹.采用改进的粒子群算法求取此类非线性规划问题,生成时间近似最优挖掘轨迹.为了验证该框架的可行性,对挖掘机工作时各关节的角度进行现场测量,将优化后的结果与现场试验数据进行对比.结果表明:提出的时间近似最优挖掘轨迹生成框架有效提高了自主挖掘机作业效率,为时间最优轨迹生成问题提供了一种解决方案.

To autonomously detect the penetration point in the working area of trench excavation, a feature detection method of penetration point based on binocular cameras was proposed. First, the homogeneous coordinate transformation is established, which can convert the 3D point cloud of the excavation area from the camera coordinate system to the excavator global base coordinate system. Then, the global gradient consistency function is designed to describe the geometric feature of the penetration point of a trench, and the position coordinates of the penetration point are detected. Finally, the test of the penetration point detection of the excavation area is conducted. Within the range of the excavation operation, the maximum position error of the penetration point detection is less than 80 mm, and the average detection error is 46.2 mm, which proves that this method can effectively detect the penetration point.

Current pavement pothole repair technology relies heavily on human experience and manual operation. To automate the repair process of pavement pothole spraying repair, a contour-guided zigzag path planning method based on depth image was proposed in this paper, including pothole slicing and slice repair path planning. In the aspect of pothole slicing, a double flood filling method was proposed to ensure the repair strategy from bottom to up under the priority of region by region, while in the aspect of slice path planning, a contour-guided round-trip repair path planning method was proposed to arrange the repair trajectory homogeneously in the pothole without exceeding its irregular boundary. The path planning results showed that the average spacing error of four kinds of common potholes with different shapes was 7.31%, the average time of the whole path planning process was 1.571 s, the average relative difference of the mean square error was only 1.83 mm, which indicated that the proposed method could obtain high path planning accuracy and fast path planning speed. The results demonstrated that the proposed contour-guided zigzag path planning method based on depth image could be applied to the automatic spraying repair of different potholes with convex or concave contours and achieve good pavement repair flatness.

Currently, polymer-derived ceramics (PDCs) often achieve electromagnetic (EM) wave attenuation by in situ generation of nanocrystals. In this paper, amorphous monolithic siliconboron carbonitride (SiBCN) ceramics with outstanding microwave absorption property were prepared via pyrolysis of hyperbranched polyborosilazane crosslinked with divinylbenzene (DVB). The crosslinking of DVB increases the ceramic yield and the sp2 carbon contents, which facilitates the formation of multiple ordered carbon structures and enhances the dielectric loss and microwave absorption property of ceramics. By regulating DVB content and pyrolysis temperature, the amorphous SiBCN ceramics with ordered carbon structures exhibit optimized microwave absorption performance with a minimum reflection coefficient (RCmin) of -54.24 dB and a maximum effective absorption bandwidth (EABmax) of 3.15 GHz. Further, when the planar sample is tested using arch method, the practical RCmin value is -14.86 dB at 14.46 GHz and the EABmax is up to 6.4 GHz (11.6 GHz-18 GHz). In addition, the amorphous SiBCN has excellent high temperature resistance, no mass change in nitrogen (N2) and only 3.23% mass loss in air until 1400 degrees C. The high temperature stable, amorphous SiBCN microwave absorption ceramics provide a new strategy to the preparation of microwave absorption materials.

The performance of unmanned excavators is related to trajectory planning. Traditional methods without integrating the excavation strategies of skilled drivers result in little or inferior improvement in efficiency and less comprehensive optimal performance than manual operation. This paper proposes a trajectory planning method that incorporates the driver's skills. First, the data are preprocessed, and topologically equivalent path points are obtained using DPA. Then, the trajectory is parameterized using cubic splines. Finally, the NSGA-II algorithm is used to solve the NLP and obtain the comprehensive optimal trajectory. The experimental results show that the method can improve excavation efficiency and motion stability.

The intelligent compaction technique uses the longitudinal acceleration signal of the roller's vibrating steel wheel to judge the soil's compaction quality. The low-frequency component of the signal is used to identify the surface stiffness of the soil and, thus, indirectly estimate the degree of compaction. High-frequency components are considered noise. However, the high-frequency component can reflect the intensity of the collisions between particles. Therefore, the high-frequency component may be more effective than the low -frequency component in evaluating the compaction quality of deep soil. This study establishes a nonlinear model using Morse wavelet transform and deep neural network to evaluate the compaction quality. The influence of high and low-frequency components on evaluation results is analyzed by controlling the frequency band range of input. The results show that, compared with the low-frequency component, the high-frequency component can more accurately evaluate the degree of soil compaction. In order to eliminate the influence of the "double jump phenomenon"of the roller, high-frequency and low-frequency components should be considered simultaneously. This method can not only accurately distinguish between under-compaction and over-compaction but also has the potential to take the actual degree of soil compaction as output.

Intelligent compaction technology monitors the compaction quality of pavement materials in real-time by observing the sensor signals installed on the roller. At present, the existing intelligent compaction detection methods cannot always accurately evaluate the comprehensive compaction quality of soil from shallow layer to deep layer. In addition, the time domain features of the signal and the frequency domain features of the power spectrum have never been considered as an observation values to evaluate the soil compaction quality. In this paper, we combine these signal features with an artificial neural network to accurately evaluate the overall soil compaction quality. First, to achieve the minimum amount of data required by an artificial neural network, each original signal is split into one hundred pieces. Then the time-domain features and frequency domain features of the signal fragment in the power spectrum is calculated and the correlation between these characteristics and the comprehensive compaction quality is analyzed. Finally, the eight features with the best correlation are used as the input of an artificial neural network to build a nonlinear model. The test results show that the nonlinear model can accurately classify the overall compaction quality of soil into three categories: under compaction, the best compaction, and over compaction.

In this paper, we propose a novel trajectory generation method for autonomous excavator teach-and-plan applications. Rather than controlling the excavator to precisely follow the teaching path, the proposed method transforms the arbitrary slow and jerky trajectory of human excavation into a topologically equivalent path that is guaranteed to be fast, smooth and dynamically feasible. This method optimizes trajectories in both time and jerk aspects. A spline is used to connect these waypoints, which are topologically equivalent to the human teaching path. Then the trajectory is reparametrized to obtain the minimum time-jerk trajectory with the kinodynamic constraints. The optimal time-jerk trajectory generation method is both formulated using nonlinear programming and conducted iteratively. The framework proposed in this paper was integrated into a complete autonomous excavation platform and was validated to achieve aggressive excavation in a field environment.

In this paper, a new autonomous excavation trajectory generation system is proposed. Instead of controlling the excavator to follow the teaching path precisely, our system replans the skillful operator's arbitrary bumpy digging and slow trajectory into a topological equivalent trajectory to ensure a fast and smooth excavation process. In this paper, the waypoints which are equivalent to the teaching path topology are found by several manual excavation tracks. The trajectory is then parameterized to obtain the minimum time-jerk trajectory under kinodynamic constraints. The trajectory generation method proposed in this paper is integrated into a complete autonomous excavation platform and the feasibility of this system is verified in a field test.