A cement-based piezoelectric composite, modified by graphene oxide (GO), was prepared to study piezoresistive capacity. The testing confirms that GO is more effective than other carbon nanomaterials at improving piezoresistive sensitivity of cement-based composites, because the content of GO in cement paste was much lower than other carbon nanomaterials used in previously published research. Further investigation indicates that the addition of GO significantly improved the stability and repeatability for piezoresistive capacity of cement paste under cycle loads. Based on experiment results, the piezoresistive sensitivity of this composite depended on GO content, water-to-cement weight ratio (w/c) and water-loss rate, since the highest piezoresistive gauge factor value (GF = 35) was obtained when GO content was 0.05 wt.%, w/c was 0.35 and water-loss rate was 3%. Finally, microstructure analysis confirmed that conductivity and piezoresistivity were achieved through a tunneling effect and by contacting conduction that caused deformation of GO networks in the cement matrix.

In recent years, deep learning-based detection methods have been applied to pavement crack detection. In practical applications, surface cracks are divided into inner and edge regions for pavements with rough surfaces and complex environments. This creates difficulties in the image detection task. This paper is inspired by the U-Net semantic segmentation network and holistically nested edge detection network. A side-output part is added to the U-Net decoder that performs edge extraction and deep supervision. A network model combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales is proposed. The model can be used for other tasks that need both semantic segmentation and edge detection. Finally, the segmentation and edge images are fused using different methods to improve the crack detection accuracy. The experimental results show that mean intersection over union reaches 69.32 on our dataset and 61.05 on another pavement dataset group that did not participate in training. Our model is better than other detection methods based on deep learning. The proposed method can increase the MIoU value by up to 5.55 and increase the MPA value by up to 10.41 when compared to previous semantic segmentation models.

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification.

In recent years, significant attention has been paid to the use of calcium sulfate whiskers (CSWs) to enhance the performance of cement-based materials (CBM). This technology has attracted widespread interest from researchers because it enhances the performance and sustainability of CBM by modifying the crystal structure of calcium sulfate. This article summarizes the fundamental properties and preparation methods of calcium sulfate whisker materials as well as their applications in cement, potential advantages and disadvantages, and practical applications and prospects. The introduction of CSWs has been demonstrated to enhance the strength, durability, and crack resistance of CBM while also addressing concerns related to permeability and shrinkage. The application of this technology is expected to improve the quality and lifespan of buildings, reduce maintenance costs, and positively impact the environment. The use of CSWs in CBM represents a promising material innovation that offers lasting and sustainable advancement in the construction industry.

With the rapid development of deep learning, pavement crack detection has started to shift from traditional manual visual inspection to automated detection; however, automatic detection is still a challenge due to many complex interference conditions on pavements. To solve the problem of shadow interference in pavement crack detection, this paper proposes an improved shadow generation network, named Texture Self-Supervised CycleGAN (CycleGAN-TSS), which can improve the effect of generation and can be used to augment the band of shadowed images of pavement cracks. We selected various images from three public datasets, namely Crack500, cracktree200, and CFD, to create shadowed pavement-crack images and fed them into CycleGAN-TSS for training to inspect the generation effect of the network. To verify the effect of the proposed method on crack segmentation with shadow interference, the segmentation results of the augmented dataset were compared with those of the original dataset, using the U-Net. The results show that the segmentation network achieved a higher crack recognition accuracy after the augmented dataset was used for training. Our method, which involves generating shadowed images to augment the dataset and putting them into the training network, can effectively improve the anti-shadow interference ability of the crack segmentation network. The research in this paper also provides a feasible method for improving detection accuracy under other interference conditions in future pavement recognition work.

In this study, a set of numerical simulation analysis methods for studying the dynamic response of runway under the action of aircraft taxiing load are presented. An aircraft-runway coupled vibration model was established, and the runway pavement roughness was taken as the vibration excitation source; then, the aircraft taxiing dynamic load was obtained. A three-dimensional finite element model of the runway was established, and the vertical dynamic displacement (VDD) response and its variation law of the runway under different void conditions were studied under the action of the dynamic load of a taxiing aircraft. In addition, using wavelet packet transform, the acceleration signals at different positions of the pavement slab under the sliding load were decomposed into three layers. The relationship among the wavelet packet energy ratio (WPER), the wavelet packet energy entropy (WPEE) of each frequency band, and the void under the pavement slab was obtained. The results show that the established model could quickly and accurately solve the aircraft taxiing dynamic load. In the case of a slight void, the VDDs in the runway center and under the main landing gears had negative exponential and logarithmic relationships with the reduction coefficient of the dynamic elastic modulus of the base layer, respectively. When there was a severe void, the pavement slab was separated from the base layer. The VDDs in the runway center and under the main landing gears were exponentially and linearly related to the slab’s void area, respectively. The vibration signals were extracted at three measuring points, and the wavelet packet energy characteristics of the signals were compared and analyzed. It was found that the WPER and WPEE of the vibration signals in the void area of the slab corner could better reflect the void state of the slab bottom.