How to quickly and accurately identify the bridge rubber bearing deterioration plays an important role in ensuring the bridge structure and road safety. This paper selects the common rubber bearings of domestic bridges as the research object, and proposes an improved YOLOv4-based bridge rubber bearing deterioration detection algorithm to address the reasons for the difficulty in detecting bridge rubber bearing deterioration due to large scale variations and small sample data sets. An image dataset (named HRBD) with annotations is constructed from real inspection scenarios, and the data is expanded by image processing means such as rotation, translation and brightness transformation, so that this dataset has sufficient data complexity and solves the problem of overfitting due to insufficient samples for network training. The anchor applicable to this dataset was regained by the K-means++ clustering algorithm, and then the CA module was inserted into the YOLOv4 backbone network for more accurate anchor localization. The improved YOLOv4 network was used for migration learning to train the dataset, and finally the trained network model was used for detection on the test set. The experimental results show that the improved YOLOv4 bridge rubber bearing deterioration detection and identification network can effectively identify and locate bridge rubber bearings and their deterioration types (crack damage, shear deformation, bearing void).

Extreme weather events have destructive impacts on ecosystems and are expected to increase. Understanding how forest ecosystems respond to climate extremes has attracted much attention in recent decades. However, the mechanisms of climate extreme under various geological conditions are still unclear and difficult to quantify. Here, we selected four extreme weather events, namely, compound dry–hot extremes, compound wet–hot extremes, compound dry–cold extremes, and extreme precipitation, to explore the memory effects of vegetation after these extremes occurred. We combined satellite observations and climate data to quantify the resistance, tolerance and resilience of vegetation using lag response time, memory strength and memory length, respectively. Our results showed that among these events, compound dry–hot extremes were the most crucial climate stressors in the Red River Basin due to longer memory length and higher memory strength. There is a trade-off between resistance (lag response time), tolerance (memory strength) and resilience (memory length). Compared with grassland, arbor and shrub forests exhibit a higher resistance and tolerance to drought and a relatively lower resilience. The tolerance to extreme precipitation of grassland surpasses that of forest, while the resistance is inferior. The vegetation in the carbonate distribution area has higher tolerance and resilience and a weaker resistance than that in the non-carbonate area. Compound hot events may accentuate the severity of the influence exerted by both extreme drought and extraordinary precipitation on vegetation. These results highlight the unique extreme weather responses of karst vegetation and supplement the theory of vegetation extreme weather response strategies.

Abstract Traditional mix design is a time-consuming and labor-intensive process ,requiring extensive testing and relying heavily on engineering experience. In order to enhance the speed and efficiency of asphalt concrete mix design process,this study investigated the use of machine learning techniques to predict key parameters of concrete mixture design,such as voids in the mineral aggregate (VMA), voids in the coarse aggregate(VCA), and dry density of the mixture(pd). Four machine learning methods, namely support vector regression, artificial neural network, random forest, and AdaBoost models were trained using data from RIOHTRack. Metircs releatde to asphalt mix design such as gradation, asphalt content, asphalt properties, compaction method, and compaction temperature were used as input variables. Various encoding methods were employed to encode classification variables, with the ordinal encoding method yielding the most favorable results. Through the calculation of different performance scoring metrics, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), and by plotting the development curve of volume parameters and asphalt content, the most suitable prediction model was selected for each target variable. The analysis revealed that the random forest model (R2 = 0.8595 for pd, R2 = 0.9488 for VMA) demonstrated the best performance in predicting pd and VMA, while the Adaboost model (R2 = 0.9716) was chosen for predicting VCA. By calculating different performance scoring metrics, such as coefficient of determination (R2), root means square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and plotting the development curve of volume parameters and asphalt content, the final prediction model was selected for each target variable. The analysis revealed that the random forest model (R2 = 0.8595 for pd, R2 = 0.9488 for VMA) demonstrated the best performance in predicting pd and VMA, while the Adaboost model (R2 = 0.9716) was chosen for predicting VCA.

Abstract A vehicle–bridge coupled vibration model, which is based on the virtual work principle and the finite element method, is established to analyze the impact of heavy-cargo transportation on a precast box girder bridge. The model can accurately simulate the dynamic characteristics of the vehicle and the bridge. A new method to reconstruct the values of deck roughness based on the International Roughness Index (IRI) is developed, after which the reconstruction is introduced to the vehicle–bridge model. Results show that the impact coefficient, which corresponds to different IRI-based reconstruction samples, obey a normal distribution. Furthermore, deck roughness played a significant role in vehicle–bridge relationship. When the heavy-cargo vehicle crossed the precast box girder bridge at low speeds, the impact coefficient changed significantly. Finally, the linear fitting formulas of the IRI-based impact coefficient are established for future research reference.

Traffic noise has become one of the most significant issues in environmental pollution complaints. In some cases, tyre/road noise level has exceeded the critical values of environmental regulations by more than 10 dB (A), and using single technical method, such as noise reducing pavement or noise barrier, cannot solve the problem effectively. In this paper, research and application were carried out by considering combination of both noise reducing pavement and barrier. Based on analysis of characteristics of tyre/road noise spectrum, a design method of sound absorption performance of barrier which will compensate the absorption ability of the pavement is proposed. Three types of noise barrier panel, namely diffusive, interference-type, and diamond-shaped sound absorption panels, were developed, which provide good complements to the absorption spectrum of porous asphalt pavement. Trail section was built for validation on a practical expressway. Noise reducing pavement designed by considering travel lane differences were firstly constructed, and noise barriers with the three different types of sound absorption panel were installed along the road section. After implementation, it shows that noise levels measured in the surrounding villages can be reduced by 10~13dB (A), and this method is proved effect for eliminating the severe pollution of tyre/road noise.

How streetscape affects subjective responses regarding acoustic comfort of pedestrian streets in traffic noise environments were examined through an experimental study that considered a multifunctional audio-visual environment with no/low, medium, and high street greenery, dominated by road traffic sound. The results showed that the differences in the acoustic comfort evaluations without and with medium or high greenery were statistically significant. The differences in the acoustic comfort evaluations among streetscapes with different environmental functions were also statistically significant. However, sound levels were the most significant factor affecting acoustic comfort when road traffic at 50-70 dBA was predominantly heard, to a lesser extent, the visible street greenery and environmental functions. Based on the negative linear relationships between sound levels and acoustic comfort evaluations for no/low, medium, and high greenery conditions, the evaluations affected by greenery increased with the increase in road traffic sound levels in transportation environments with accessing and transportation transfer functions, whereas those influenced by either high or medium greenery in leisure environments with resting and catering functions tended to decrease gradually.

This research aims to analyze the distribution and evolution of pores within the planar structure of cement-based materials. Utilizing digital imaging methods, a model for pore plane distribution was established, and the evolutionary patterns of both total pore numbers and varying pore sizes in cement-based materials were investigated. The research introduced an innovative experimental method for analyzing pore distribution within cement-based planar structures. Additionally, a hybrid method was proposed, combining automated image binarization thresholding with manual comparative analysis, thereby enhancing the feasibility of comparative research. Pores were categorized into four distinct sizes: tiny pores (5–200 μm), small pores (200–500 μm), medium pores (500–1000 μm), and large pores (>1000 μm). Areas with apertures <5 μm were classified as dense areas. The findings indicated that the overall number of pores in cement-based materials increased due to the influence of styrene butadiene latex additives. However, at a 15% dosage, the rate of pore formation reached an inflection point, confirming that various factors, such as styrene butadiene latex, air bubbles, and the cement-based material itself, collectively influenced pore formation. The research also demonstrated that styrene butadiene latex affected the four categorized pore sizes differently. Importantly, a higher latex dosage did not necessarily lead to a proportional increase in pore content. Pore content was influenced by multiple factors and exhibited different distribution patterns. The number of micropores, although relatively small, gradually increased with higher latex dosages, while small and medium pores generally showed an upward trend. At a 10% latex dosage, both small and medium pores reached a turning point in their rate of increase. Large pores also exhibited a general increase, peaking at a latex dosage of 10%. It was confirmed that both the total pore volume and the content of micropores were critical factors in determining the mechanical properties of cementitious materials. Higher porosity and micropore content generally weakened mechanical performance. However, at a small latex dosage, there was an improvement in flexural strength. When the latex dosage reached 15%, the total pore and micropore content declined, resulting in a balanced increase in flexural strength and a mitigated decline in compressive strength. This study offers valuable insights into the evolution of total pore volume and the content of pores of various sizes, providing a theoretical basis for the meticulous selection of additive types and dosages from a microscopic perspective.

Permeable asphalt pavement refers to an asphalt mixture layer with an air void content of more than 18% and internal water permeability and drainage capabilities that can quickly drain away water on the road surface, improve rainy day travel safety, and improve ride comfort. This paper aims to explore the optimal asphalt mixture design for long longitudinal slope pavement (referred to as the FAM mixture). By using CT scanning technology to analyze the air void content of different rotated and compacted asphalt mixture specimens and extensively testing and evaluating the performance of permeable pavement mixtures, the following conclusions are drawn: Based on the research philosophy of functional integration, a new asphalt mixture gradation suitable for long longitudinal slope roads is proposed, with the optimal key factor composition being: 0.075 mm passing rate of 7%, 2.36 mm passing rate of 20%, 9.5 mm passing rate of 55%, and an oil-stone ratio of 4.8%. The FAM mixture was divided into three parts for air void analysis, with the upper part having a slightly higher air void content than the lower part. The air void distribution diagram of the FAM mixture is concave, with higher air void rate curves on both sides and a lower middle curve. Through dynamic modulus testing, the strength requirement for the road asphalt mixture in the pavement structure design was evaluated. It was found that at high temperature conditions (50 °C), the minimum dynamic modulus value of the FAM mixture was 323 MPa, with a peak value of 22,746 MPa at a temperature of −10 °C and a frequency of 25 HZ. The dynamic modulus value at high temperature conditions is lower than at low temperature conditions, while the dynamic modulus value at high frequency conditions is higher than at low frequency conditions. This study provides useful information and experimental data for the design of new asphalt mixtures for long longitudinal slope roads and has conducted in-depth research on the air void distribution and performance of the mixture, providing strong support for related research fields and practical applications.