Grouting pavements are susceptible to cracking, which can significantly reduce their service life. For this reason, water-borne epoxy resin (WER) was added to the grouting materials to improve the performance of grouting asphalt mixtures. Grouting materials with different levels of WER were prepared and characterised for their flow properties, setting time, mechanical strength and micromorphology. The road performance of the grouting asphalt mixtures was evaluated through wheel tracking test, low-temperature bending test and water immersion Marshall test. The results show that, WER can form a membrane structure on the surface of hydration products, improving the flexibility of grouting materials. However, it also delays the setting time of grouting materials. The addition of WER slightly diminishes the high-temperature performance of grouting asphalt mixtures, but improves the low-temperature cracking resistance and water stability of grouting asphalt mixtures. In particular, 7.5% WER increases the low-temperature destructive strain of grouting asphalt mixtures by 29.2%.

To tackle the computational cost and registration time challenges in traditional point cloud registration methods like ICP (iterative closest point) such as LIO-SAM (tightly-coupled lidar inertial odometry via smoothing and mapping) and newer models utilizing deep neural networks such as HRegNet(hierarchical registration network), a lightweight and real-time HKRNet (hierarchical kcpstack registration network) network model was proposed. The model was developed by thoroughly studying the HRegNet neural network point cloud registration framework. Initially, a combined filtering approach involving point cloud voxelization and Gaussian threshold downsampling was used to remove redundant points from ground radar scans, reducing the point count from around 130 000 to about 70 000. Subsequently, the computationally intense KNN (K-nearest neighbors) point cloud clustering algorithm within the HRegNet model was enhanced by optimizing it to a KD-Tree (K-dimensional tree) algorithm, resulting in a 25% improvement in processing speed while upholding accuracy. Lastly, to address high memory usage and low computational efficiency of the convolutional modules in the model, a lightweight convolutional module leveraging tensor decomposition and a hierarchical singular value decomposition algorithm was introduced. This leaded to a compressed model size of 86.1% of the original and a decrease of 61.2% in computational cost. The outcomes indicate that the HKRNet network, in comparison to the HRegNet network, can reduce registration time by 40% with minimal loss of accuracy, achieving a single registration time not exceeding 84ms, thus meeting real-time registration requirements.

Because Hainan Province is located in the tropics, it is often hit by extreme weather such as typhoons and rains, so it is very prone to geological disasters such as slope collapse and landslides, which eventually cause irreparable losses. In order to improve the stability of tropical soil slope in a green and environmentally friendly way, the slope was strengthened by microbial induced calcium carbonate precipitation (MICP) technology and carpet grass root slope consolidation. Suitable microbial strains were first screened out, and the preparation process of related microbial agents was optimized. Subsequently, carpet grass root system was implanted in the slope soil indoors, and MICP treatment was carried out after the formation of the root-soil complex. Subsequently, a series of laboratory tests and numerical simulation analyses were carried out to evaluate the reinforcement effect of this technology. The results showed that MICP technology and plant root treatment complemented each other in terms of mechanical brittleness and integrity of slope, and significantly improved the unconfined compressive strength and shear strength performance of soil, effectively enhanced the stability of slope, and reduced the risk of slope erosion. Finally, the numerical simulation verification was carried out by using Abaqus finite element software, which enhanced the reliability of the research results. It can be seen that the MICP combined with carpet grass root reinforcement method provides an effective reinforcement method for tropical soil slopes, which not only improves the mechanical properties of slopes, but also promotes ecological restoration and environmental sustainability. This result provides a new technical approach for the ecological reinforcement of slopes in tropical areas, and has certain theoretical and practical significance for protecting the ecological environment and reducing the risk of geological disasters.

The transformation of sludge into biochar adsorbents for the removal of tetracycline contaminants in water bodies represents one of the effective approaches for the resource utilization of sludge and enables the realization of the circular economy concept of “treating waste with waste”. Municipal sludge was employed as the raw material, and sludge biochar was fabricated through pyrolysis for the adsorption and removal of tetracycline. The adsorption and removal efficacy of tetracycline was investigated, and the preparation conditions of sludge biochar and adsorption environmental conditions were optimized. Additionally, by combining methods such as scanning electron microscopy, infrared spectroscopy, and BET(Brunauer,Emmett,Teller) specific surface area testing, the structural characteristics of sludge biochar and the underlying mechanism of its adsorption behavior towards tetracycline were explored. The results indicate that the sludge biochar prepared under a pyrolysis temperature of 800 ℃ and a pyrolysis duration of 4 hours exhibits the optimal adsorption performance for tetracycline. The pH value exerts a significant influence on the adsorption effect. In a weakly acidic environment, the adsorption effect of sludge biochar on tetracycline is the most favorable, with a maximum adsorption capacity reaching 45.33 mg/g. Thermodynamic and kinetic analyses demonstrate that the pseudo-second-order kinetic model and the Langmuir adsorption isotherm model can appropriately fit the adsorption process of tetracycline by sludge biochar. The adsorption process is primarily monolayer adsorption, dominated by surface chemical adsorption. In conjunction with the analysis of characterization test results, the chemical adsorption mainly involves processes such as electrostatic attraction, cation exchange, complex precipitation, π-π conjugation, and hydrogen bonding. Simultaneously, the pore structure characteristics of sludge biochar result in the adsorption process of tetracycline also encompassing pore filling and Van der Waals force.

In order to solve the problems of the traditional interactive multiple model (IMM) algorithm in vehicle target tracking, such as the model probability change is not obvious and the tracking accuracy is insufficient, an improved adaptive IMM-UKF(unscented Kalman filter) algorithm was proposed. Firstly, the vehicle motion model was established by using uniform speed straight line, uniform acceleration straight line and uniform turning, and the vehicle target was tracked by unscented Kalman filter. Then, the probability change rate of sub model was used as the correction parameter of IMM algorithm, and different correction strategies were adopted for the main diagonal and non main diagonal elements of Markov matrix. Finally, the decision window was set to modify the main diagonal element of the normalized Markov matrix to expand the probability of matching model. The results show that the probability of the improved algorithm model changes more obviously, and the root mean square errors of position and velocity are less than the original algorithm, which effectively improves the tracking accuracy.

Seasonal segmentation of building electricity consumption time series (BECTS) is of great significance for accurate load forecasting and pattern mining. Aiming at the problem that accurate BECTS seasonal segmentation is difficult to be realized by traditional timing segmentation, fixed-temperature segmentation and adaptive five-days temperature segmentation methods, a new adaptive seasonal segmentation method for BECTS based on Toeplitz inversed covariance-based clustering (TICC) was proposed. The method was based on the binary time series of building hourly electricity load and outdoor dry bulb temperature, and the TICC algorithm was used for real-time segmentation and clustering. A large public building electricity load case in a hot summer and warm winter area was analyzed, and the result showed that the similarity between samples of the same type and the difference between samples of different types were enhanced by the method. Compared with the timing segmentation, fixed-temperature segmentation and adaptive five-days temperature segmentation methods, the average dynamic time warping (DTW) distance of each category after TICC segmentation was improved respectively by 46.54%, 35.73% and 7.59%. This method can be used as data preprocessing to provide data support for single building data mining analysis, such as building electricity consumption pattern mining and load forecasting.

To investigate the flexural bearing capacity of timber-concrete composite beams, Yunnan pine was selected as the base material, and high-strength self-tapping screws were used as shear connectors. Self-tapping screws were drilled into the timber to connect with cast-in-place concrete slabs, with partial slotting of the connection surface on the timber beam as a variable, to study the flexural performance of timber-concrete composite beams. Two groups of four specimens were designed in total, and a stepwise loading method was adopted to conduct four-point bending tests to analyze the mechanical properties of the composite beams. The results indicated that the overall performance of the composite beams was good, with the partially slotted design exhibiting better flexural stiffness and overall performance compared to the unslotted beams. Under the same load conditions, the deflection of the partially slotted beams was reduced by 57% compared to the unslotted beams, and the interface slip was reduced by 50%. Theoretical analysis results were in good agreement with the experimental findings, showing that the effective flexural stiffness and composite effect coefficient of the partially slotted beams were higher than those of the unslotted beams. The partially slotted design of the composite beams demonstrated superior overall performance, as well as improved stiffness and strength.

To enhance market acceptance of construction waste recycling products, the impact of awe on consumers’ purchasing willingness investigated was investigated. Using an emotion assessment scale, purchase intention scores, and fNIRS, along with a virtual purchase experimental setup, the effects of awe induced by nature videos on subjects’willingness to purchase construction waste recycling products were examined. Measurements were conducted to separately assess the emotions induced by nature videos, the willingness to purchase construction waste recycling products, and the changes in brain activity during the viewing of nature videos. The results from the emotion assessment scale revealed that nature videos significantly induced awe emotions. The fNIRS data demonstrated deactivation in the brain's default mode network (DMN), associated with self-processing. This suggests that the experience of awe may be linked to reduced self-consciousness. The scoring data indicated that the awe experienced significantly enhanced the subjects' willingness to purchase construction waste recycling household products, however, the subjects' willingness to purchase construction waste recycling materials were not being significantly influenced by awe. Therefore, in construction waste recycling household product marketing, leveraging awe through natural videos can increase the willingness to purchase construction waste recycling household products, subsequently improving its market acceptance.

To address the issue of land and capital waste caused by suboptimal site selection and construction models for urban drone landing and takeoff sites, the maximum coverage model is initially used for site selection. However, due to the uneven distribution of demand points and overly simplistic coverage determination criteria, the results show low coverage rates and overly concentrated site selection. To solve this problem, a method based on spatially continuous demand for the maximum coverage model of drone landing and takeoff site selection was proposed, considering factors such as no-fly zones and application scenarios. Demand objects were determined using a regular grid, and candidate sites were identified using the PIPS(polygon intersection point set) method. The feasibility of the improved model was validated through a case study of site selection for urban drone landing and takeoff sites in Binhai New Area, Tianjin. When the number of landing and takeoff sites was fixed at 14, the improved model increased the actual service area coverage rate from 62.03% to 88.61%. The results indicate that this method better meets the practical requirements for drone landing and takeoff site selection, resulting in more evenly distributed and rational site layouts, and significantly enhancing the service coverage rate of the drone landing and takeoff sites.

Under the context of the rapid rise of smart airports, the widespread deployment of autonomous vehicles requires an efficient safety operation system. In order to develop a collision warning method based on collision probability for airport unmanned driving vehicles, using ADS-B data as a foundation, considering the interaction between aircraft and vehicles at taxiway segments and intersections. The collision probability analysis was conducted for these two types of interactive environments. Through the analysis of single-vehicle warning simulation diagrams, different levels of warning thresholds were set. When the collision probability was 0.3≤p(c)≤0.5, the following vehicle entered the secondary warning state, and the vehicle braking acceleration took a value range of 0.5~1.5 m/s². When p(c)＞0.5, the following vehicle entered the primary warning state, and the vehicle braking acceleration took the maximum value of 2 m/s², and carrying out simulation analysis for the same taxiway and intersection according to the set warning threshold, the simulation test showed that the collision warning method based on collision probability could calculate the probability of collisions occurring during vehicle movement on the taxiway, and perform deceleration braking according to the corresponding warning threshold, effectively reducing the possibility of collision accidents. Through Monte Carlo random simulation experiments, the collision probability change diagram under different driving modes at crossroads was obtained, and the effectiveness of the warning algorithm was verified by using hierarchical warnings for simulation analysis. The simulation experiment proved that regardless of the driving mode, the warning algorithm could effectively avoid collision conflicts, further proving that the proposed method had high adaptability. A collision probability-based collision warning method was constructs for airport unmanned driving vehicles, which can effectively avoid the occurrence of airport field collision conflicts. Meanwhile, it can significantly improve the safety of unmanned driving vehicles in the airport environment.