Foam concrete is widely used for its excellent properties, such as light weight, heat insulation, fire resistance, and sound insulation. The stability of foam is the main factor that affects the mechanical performance of foam concrete. The experiments are designed from two perspectives: the foam’s stability performance and the foam concrete’s modification effect. The effects on foam volume, foam half-life, foam bleeding rate, and foam pore size were investigated based on different concentrations of foam stabilizer CMC (0%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%). A combination of macroscopic testing and microscopic analysis, a comparative study of dry density, water absorption test, mechanical property test, and pore structure analysis were conducted after using the modified foam for foam concrete. It is shown that the addition of CMC has an enhanced effect on foam stability. Foaming volume, water secretion rate, and average pore size showed a decreasing trend with the increase of CMC admixture, while the foam half-life displayed an increasing trend. In addition, adding CMC reduces the dry density and improves water absorption and compressive strength. The pore structure development of foam concrete has a noticeable improvement effect, and the optimal amount of admixture is 0.4%. Research results provide a reference for applying thickening foam stabilizer CMC in foam concrete.

Recycled aggregate concrete has received increasing attention owing to its broad development prospects in recent years. This study discusses the enhancement mechanism of various fibers on the mechanical properties, high-temperature resistance, and freeze–thaw cycle resistance of recycled aggregate concrete. It reviews the effects of fiber types and content on the strength, failure state, and resistance to recycled aggregate concrete’s high and low temperatures. The results indicate that fibers can significantly improve the flexural strength and tensile strength of recycled aggregate concrete in the bridging effect but have little effect on compressive strength. Regarding high-temperature resistance, fibers with a lower melting point can form channels in the concrete, reducing the internal pressure of water vapor. Fibers with higher melting points can act as bridges, inhibiting the generation and propagation of cracks in recycled aggregate concrete. Therefore, fiber-reinforced recycled aggregate concrete can perform better at higher temperatures than ordinary recycled aggregate concrete. Due to the high water absorption rate in recycled aggregate concrete, which is approximately 7–10 times that of natural aggregate concrete, it is easier to reach the critical water saturation of freeze–thaw damage. Results show that 0.2 kg/m³ polypropylene fiber and 1.2 kg/m³ basalt fiber show excellent performance in improving the frost resistance of recycled aggregate concrete.

In the process of research and development of self-healing concrete, it is observed that there are three main factors controlling the self-healing effect of concrete: first, the bacteria with repair ability and strong vitality; Second, the carrying capacity of the carrier and the matching degree with concrete; The third is the concentration of bacteria. This paper focuses on the mechanical properties of Bacillus subtilis self-healing concrete with sisal fiber, PVA, and expanded perlite as the carrier. To better study the mechanical properties of self-healing concrete caused by the carrier, the experiment adopts the design parameters of C30 concrete and conducts experiments on compressive resistance, flexural resistance, freeze-thaw cycle, and sulfate corrosion resistance to analyze the influence of different carriers on the mechanical properties of self-healing concrete, and obtains the best carrier. The concentration gradients of three groups of bacterial solution were set as 2od, 2.5od, and 3od, respectively, for comparison to avoid the influence of bacterial concentration. It compared the impact of bacterial solution concentrations on the specimen's mechanical properties, and the effect of carriers was also analyzed. The experimental results show that the mechanical properties of the specimen using 2.5od bacterial liquid concentration with PVA as the carrier have peaked. With the increase in bacterial solution concentration, the specimens' comprehensive mechanical properties increased first and then decreased. The compression resistance of the specimen with PVA is higher than that of the specimen with sisal fiber and expanded perlite. At the same time, the frost resistance and corrosion resistance of the PVA carrier specimen is also higher than that of the specimen with sisal fiber and expanded perlite carrier.

The YOLO (You Only Look Once) series has recently demonstrated remarkable effectiveness in the domain of object detection. However, deploying these networks for concrete bridge defect detection presents multiple challenges, such as insufficient accuracy, missed detections, and false positives. These complications arise chiefly from the complex backgrounds and the significant variability in defect characteristics observed in bridge imagery. This study presents BD-YOLOv8s, an advanced methodology utilizing YOLOv8s for bridge defect detection. This approach augments the network’s adaptability to a broad spectrum of bridge defect images through the integration of ODConv into the second convolutional layer, processing information within a four-dimensional kernel space. Furthermore, incorporating the CBAM module into the first two C2F architectures leverages spatial and channel attention mechanisms to focus on critical features, thus enhancing the accuracy of detail detection. CARAFE replaces traditional upsampling methods, improving feature map reconstruction and significantly reducing blurs and artifacts. In performance assessments, BD-YOLOv8s attained 86.2% mAP@0.5 and 56% mAP@0.5:0.95, surpassing the baseline by 5.3% and 5.7%. This signifies a considerable decrease in both false positives and missed detections, culminating in an overall improvement in accuracy.

Target detection in Unmanned Aerial Vehicle (UAV) aerial images has gained significance within UAV application scenarios. However, UAV aerial images present challenges, including large-scale changes, small target sizes, complex scenes, and variable external factors, resulting in missed or false detections. This study proposes an algorithm for small target detection in UAV images based on an enhanced YOLOv8 model termed YOLOv8-MPEB. Firstly, the Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is substituted with the lightweight MobileNetV3 backbone network, consequently reducing model parameters and computational complexity, while also enhancing inference speed. Secondly, a dedicated small target detection layer is intricately designed to optimize feature extraction for multi-scale targets. Thirdly, the integration of the Efficient Multi-Scale Attention (EMA) mechanism within the Convolution to Feature (C2f) module aims to enhance the extraction of vital features and suppress superfluous ones. Lastly, the utilization of a bidirectional feature pyramid network (BiFPN) in the Neck segment serves to ameliorate detection errors stemming from scale variations and complex scenes, thereby augmenting model generalization. The study provides a thorough examination by conducting ablation experiments and comparing the results with alternative algorithms to substantiate the enhanced effectiveness of the proposed algorithm, with a particular focus on detection performance. The experimental outcomes illustrate that with a parameter count of 7.39 M and a model size of 14.5 MB, the algorithm attains a mean Average Precision (mAP) of 91.9 % on the custom-made helmet and reflective clothing dataset. In comparison to standard YOLOv8 models, this algorithm elevates average accuracy by 2.2 percentage points, reduces model parameters by 34 %, and diminishes model size by 32 %. It outperforms other prevalent detection algorithms in terms of accuracy and speed.

Engineered Cementitious Composite (ECC) is currently receiving more and more attention due to its excellent tensile strain hardening and multiple cracking properties. However, due to the high material cost of polyvinyl alcohol (PVA) fiber and quartz sand, its widespread promotion and application in the market are limited. Therefore, scholars at home and abroad have conducted many active studies on improving ECC. This paper summarizes the development history and research status of ECC materials, summarizes the current domestic and foreign researchers’ improvement methods for ECC materials, and classifies the improvement methods into three categories: the type of fiber variation, the water-binder ratio variation and adding mineral admixtures, the influences of the above three factors on the mechanical properties and durability of ECC, such as compressive and flexural resistance, are described in detail, and the mechanism of action is explained. Furthermore, this paper introduces the most common uniaxial compression and uniaxial tension constitutive models of ECC. They are briefly classified and evaluated, hoping to help readers’ numerical simulation analysis. Finally, some suggestions for ECC research, such as the proportion of water binders and the application of composite fibers, require further research.