Reclaimed asphalt pavement (RAP) has been utilized in numerous road projects. However, considering safety and service life, further in-depth investigation is required into the road performance of high dosage RAP hot-mix/warm-mix recycled asphalt mixtures under traffic loads. To investigate this, indoor tests and mathematical analysis were employed to examine the impact of design parameters, including the waste cooking oil (WCO) content, RAP dosage, and mixing method, on the road performance of recycled asphalt mixtures. The results indicate that in most cases, the VMA of R-100RAP mixes exceeds 14%, and the VV exceeds 3%. Moreover, both the VV and VMA of asphalt mixtures with 60% RAP content, 10% WCO, and 1.0% asphalt fall within specified ranges. Additionally, the split strength ratios of the majority of R-100RAP, R-80RAP, W-80RAP, and R-60RAP mixes surpass 80%, indicating excellent resistance to water damage. The addition of WCO proves beneficial in enhancing the water damage resistance of recycled asphalt mixtures. Although the rutting resistance of the W-80RAP asphalt mixture was inferior to that of the recycled asphalt mixtures containing R-80RAP and R-60RAP, all three exhibited commendable rutting resistance. When comparing the performance of W-80RAP and R-60RAP, it is observed that W-80RAP exhibits a smaller phase angle and a larger stiffness value, indicating better aging resistance. In order to maximise performance and facilitate the effective building of high dosage RAP hot-mix/warm-mix recycled asphalt mixes, the research provides a theoretical framework.

The emergence of aerial building machines has greatly improved the environment and efficiency of high-rise building operations, while also facing challenges such as increased difficulty in construction operations and complex construction processes. With the continuous deepening of digital transformation in the construction industry, the digital expression of building machine construction processes has emerged as an intuitive and clear solution. It significantly enhances the transparency of the construction process, optimizes resource allocation, and strengthens decision support for project management. An effective pathway for the digital expression of building machine construction processes was established, aimed at advancing high-rise construction towards intelligent management. Through theoretical foundations and field research analysis, the needs for the digital expression of building machine construction processes were identified, leading to the design of a framework for implementing digital expression of building machine construction processes. This research not only provides theoretical guidance for the digital transformation of building machine construction processes but also expands new perspectives on the application of knowledge graphs, interactive electronic technical manuals, model-based definition (MBD) techniques, and augmented reality(AR) technology in the construction field.

The issue of comfort in subway stations is typically analyzed using the predicted mean vote (PMV) and the predicted percent dissatisfied (PPD) indices. Based on the PMV-PPD comfort indices calculation model, the weight proportions of different environmental parameters on comfort were studied. The PMV calculation model was improved by considering the spatiotemporal characteristics of passengers’ clothing and activity levels. The PPD calculation model was enhanced by taking into account the impact of drastic environmental temperature changes on comfort. The environmental comfort of public areas in stations was then analyzed using the improved PMV-PPD calculation model. On this basis, the feasibility of predicting environmental comfort using long short-term memory (LSTM) networks was explored. The research results indicate that the weight proportions of metabolic rate, air temperature, clothing thermal resistance, and humidity on environmental comfort are 0.558, 0.260, 0.113, and 0.069, respectively. At a given time, the maximum differences in PMV and PPD at different monitoring points on the platform are approximately 15% and 60%, respectively. The improved PMV-PPD calculation model is found to be more universally applicable compared to the traditional PMV-PPD calculation model. The neural network is shown to accurately predict PMV and PPD values, with a maximum error of 8% for PMV and 14% for PPD between the actual and predicted values.

Qiandongnan is the largest and best-preserved Miao settlement area in China, holding significant ethnic cultural heritage. Traditional settlements form an essential part of this heritage. Studying their spatial characteristics and influencing factors is crucial for the sustainable development and protection of cultural heritage in this region. By comprehensively utilizing ArcGIS spatial analysis, boundary morphology index, spatial syntax, and geographic detector methods, the spatial characteristics of settlements were deconstructed from the perspective of regional pattern and case feature analysis, and their influencing factors were explored. The results indicate that the spatial distribution of traditional Miao settlements in Qiandongnan is characterized by significant agglomeration and hierarchy. The highest nuclear density is at the intersection of Leishan, Taijiang and Jianhe. The overall spatial pattern shows a “dense in the southwest and central-south, sparse in the northeast” distribution. Constrained by natural geography, the settlements are mainly distributed in the Qingshui River and Duliu River valleys at altitudes of 500~1 000 m, with undulations of 10~20 m, gradients of 2°~5°, and sunny slopes of 90°~270°. The settlements’ spatial structure exhibits a “clustered” distribution with finger-like external boundaries, and the center shows differentiated traffic flow within and at the edges of the settlements. The geodetector study reveals that Miao traditional settlements are regional spatial carriers of a natural-economic-social complex system. The natural geographic environment fundamentally shapes spatial patterns, the social environment guides and controls internal spatial organization and evolution, and economic development decisively influences spatial development and protection. The study enhances the understanding of this complexity, which is vital for appreciating Miao culture, developing strategies for protecting and developing this cultural heritage, and implementing rural revitalization strategies.

At present, the renovation of old residential areas is in a comprehensive promotion stage. Building a systematic and scientific external space renovation system for old residential areas is of great significance for promoting the renovation of old residential areas, improving the quality of life of residents, and optimizing urban image. A comprehensive transformation system covering five criteria layers and twenty-three subcategories was organized and constructed based on a literature review and keyword clustering analysis. Secondly, the weights of various renovation elements from different perspectives were quantitatively analyzed using a questionnaire survey and analytic hierarchy process. At the standard level, residents pay more attention to facility renovation and improving community service quality, with evaluation weights of 0.256 9 and 0.223 1, respectively. Planning and design management personnel pay more attention to transportation and environmental renovation, with evaluation weights of 0.238 2 and 0.231 7, respectively. The factor layer weights indicate that all entities emphasize the importance of landscape greening, activity space quality, environmental sanitation facilities, and facade renovation, which should be given special attention in the external space renovation of old residential areas.

Accurate prediction of wind speed along high-speed rail lines is a fundamental requirement for railway disaster warning systems. To enhance the capability to respond to and handle sudden events caused by strong winds, a short-term wind speed prediction method based on the subtraction average based optimizer (SABO) algorithm optimized long short-term memory (LSTM) neural network was proposed. Firstly, considering the nonlinearity and non-stationarity of wind speed, the min-max (MM) method was used to normalize the wind speed data. Secondly, the “-v” method in the SABO algorithm was employed to search and optimize the key parameters of the LSTM model, constructing a wind speed prediction model. Finally, the effectiveness of the model was tested using measured wind speed data collected from wind speed collection points along the Baoji-Lanzhou high-speed railway in China. Experimental results show that the SABO algorithm’s optimization effect is better, and the prediction accuracy is higher. The average absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the constructed model are 11.96%, 1.23%, and 16.47%, respectively, with a coefficient of determination (R²) of 0.995. Compared to other models, the LSTM neural network optimized by the SABO algorithm exhibits better fitting performance and higher prediction accuracy in short-term wind speed prediction, providing a new method and approach for wind prediction and warning along high-speed railway.

In order to address the issues of low accuracy and high missed detection rates in existing pavement crack detection algorithms, an improved pavement crack detection algorithm based on YOLOv8n, named YOLO-CD (YOLO-crack detection), has been proposed. The scale sequence feature fusion (SSFF) module and triple feature encoder (TFE) module from the ASF-YOLO architecture were utilized by the YOLO-CD algorithm to enhance the detection performance for multi-scale cracks and the perception capability of target features. Additionally, the coordinate attention(CA) mechanism was introduced at the end of the backbone network and in the neck network, with positional information embedded into channel attention, thereby strengthening the extraction capability of crack features. Furthermore, an additional P2 small object detection layer was added on top of the original three output layers of YOLOv8n, increasing the multi-scale receptive field of the network, allowing both global and local context information to be captured simultaneously, thereby improving the detection capability for small cracks in complex scenes. The original YOLOv8n detection head was replaced by the DyHead detection head, achieving the integration of scale, spatial, and task attention mechanisms, and further enhancing the network’s detection performance for cracks. Experimental results show that in the self-built PD-Dataset, the mAP50 of the improved YOLO-CD algorithm is increased by 4.1% compared to the original YOLOv8n algorithm. In the public dataset RDD2020, the mAP50 of the improved YOLO-CD algorithm is increased by 1.5% compared to the original YOLOv8n algorithm. Moreover, the algorithm’s detection speed is found to reach 89.9 frames/s, meeting the real-time requirements of pavement crack detection.

In order to effectively predict the fuel consumption of vehicles, improve fuel economy and promote energy saving and emission reduction, a Hyperband-CNN-BiLSTM-based motor vehicle fuel consumption prediction method was proposed. Firstly, based on the vehicle operating status data and fuel consumption data collected from the actual road test, the salient factors affecting the fuel consumption of vehicles were analyzed. Secondly, combining the powerful feature extraction capability of convolutional neural network(CNN) and the advantages of bidirectional long and short-term memory network (BiLSTM) in dealing with the time-series data, a combined model of vehicle fuel consumption prediction based on CNN-BiLSTM was constructed. Then, in order to improve the model prediction accuracy, the combined model was optimized by Hyperband optimization algorithm, and the vehicle fuel consumption influencing factors were taken as the model input features to train the model to realize the modeling and prediction of vehicle fuel consumption. Finally, CNN, LSTM, BiLSTM, CNN-LSTM and CNN-BILSTM were selected as comparison models to evaluate the effect of Hyperband-CNN-BiLSTM prediction model. The results show that compared with other models, the Hyperband-CNN-BiLSTM model has the smallest mean absolute error (MAE) and root mean squared error (RMSE). They are 0.057 69 and 0.119 25, respectively. R² is the largest (0.991 76), and the model has the best prediction effect.

To reveal the complex relationship between the built environment and walking activity among older adults, a gradient boosting regression tree (GBRT) model was adopted, combined with multi-source data such as mobile signaling data, remote sensing image data, and point of interest (POI), to deeply explore the non-linear impact of the built environment on elderly walking activities and its threshold characteristics. The findings indicate that the built environment has a significant nonlinear impact on older adults’ walking activity, with land use factors being the most influential. Specifically, land use mix, the proportion of commercial service facilities, and the proportion of residential land are identified as key factors affecting older adults' walking activity. Additionally, the proximity of facilities also plays an important role. Finally, suggestions have been put forward for the adaptive transformation of land use and facilities to improve the level of elderly walking activities and promote healthy aging.

In order to determine the reasonable spacing of underground interchanges, a series of traffic simulation experiments with varying spacing cases was conducted in VISSIM using the Wenhui Street underground interchange of the Two Lakes Tunnel in Wuhan City as a practical case. The effects of interchange spacing, mainline and ramp traffic volumes and design speeds on crash risk at the diverging and merging areas of the underground interchanges were analyzed. Then, the crash risk variation at the diverging and merging areas of the underground interchanges was predicted using the four typical machine learning algorithms, i.e., extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and multilayer-perceptron (MLP). Results show that when the spacing increases from 1.5 km to 2.5 km, travel time, average delay, average queue length, and traffic conflict rate decrease significantly, and the collision risk index of time-to-collision(TTC) increase significantly. When the spacing is above 2.5 km, the decreases in travel time, average delay, average queue length, and traffic conflict rate start to slow down, and so does the increase in TTC. When the spacing is 2.5 km and above, the overall traffic operation efficiency and safety of the underground interchange increase significantly. The XGBoost model can predict the crash risk variation reaching a precision of 88.3%. This study can be a theoretical support and practical case for the setting of underground interchange spacing.