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Improper layout of drainage structures and inadequate insulation measures in cold tunnels can result in varying degrees of frost formation during operation. This study focuses on the Hongtoushan highway tunnel as an example, where the distribution characteristics of the temperature field around the lower drainage structure under different arrangements are investigated through indoor model testing. The results indicate that there is a significant hysteresis phenomenon in temperature changes across the cross-section as the burial depth increases. With an increase in the burial depth of the surrounding rock, the hysteresis time of temperature changes gradually elongates. The temperature variation pattern can be approximated by a cubic polynomial. In the vertical section, as the tunnel depth increases, the temperature of the surrounding rock in the lower part of the tunnel gradually rises while the amplitude of temperature change diminishes. The temperature near the centerline is relatively lower compared to the sides, where the temperature gradually increases moving away from the centerline.

Historical districts are the mark of the continuity of urban history and are non-renewable. Typhoon disasters rank among the most serious and frequent natural threats to China’s coastal regions. Improving the wind resilience of China’s coastal historical districts is of great significance for their protection and inheritance. Accurately analyzing the different characteristics of the influencing factors of wind resilience in China’s coastal historical districts can provide a theoretical basis for alleviating the damage caused by typhoons and formulating disaster prevention measures. This paper accurately identifies the main influencing factors of wind resilience in China’s coastal historical districts and constructs an influencing factor system from four aspects: block level, building level, typhoon characteristics, and emergency management. An IIM model for the systematic analysis of influencing factors of wind resilience in China’s coastal historical districts based on the Improved Decision Making Trial and Evaluation Laboratory (IDEMATEL), Interpretive Structural Modeling (ISM), and Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) methods is established. This allows us to explore the mechanism of action of internal influencing factors of typhoon disasters and construct an influencing factor system, in order to propose prevention measures from the perspective of typhoon disaster characteristics and the overall perspective of China’s coastal historical districts. The results show that the driving force of a building’s windproof design in China’s coastal historical districts is low, but its dependence is strong; the driving forces of block morphology, typhoon level, and emergency plan are strong, but their dependence is low. A building’s windproof design is a direct influencing factor of the wind resilience of China’s coastal historical districts; block morphology, typhoon level, and emergency plan are the most fundamental and key influencing factors of the wind resilience of China’s coastal historical districts.

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%.

Achieving synergistic effects in pollution reduction and carbon mitigation is a major national strategy for China. Given the common origins and processes of air pollutants and greenhouse gases, this study constructs a theoretical framework for the study of the synergistic effects of air pollution and carbon emissions. Based on the coupling coordination degree model and the geographically and temporally weighted regression model, it identifies significant factors influencing the synergistic effects of air pollution and carbon emissions and their varying mechanisms of action. Results are as follows: 1) The spatial and temporal trends of PM 2.5 pollution and carbon emissions in the Wuhan metropolitan area exhibit homogeneity. The coupling coordination degree between air pollution and carbon emissions shows an initial increase followed by a decrease over time and a spatial pattern of “local clustering of areas with medium–high-level coupling coordination”. 2) Twelve factors significantly impact the synergistic effects of air pollution and carbon emissions at the county level in the Wuhan metropolitan area: number of inversion days, precipitation, temperature, vegetation coverage, number of green patents, total population, regional GDP, per capita regional GDP, proportion of secondary industry, total nighttime light, energy consumption efficiency and built-up area. 3) The impact intensity of these factors on the synergistic effects of air pollution and carbon emissions varies not only over time but also across different regions within the same year. Regions with strong impact forces shift over time. This manuscript provides a solid foundation for theoretical research on and practical strategies for advancing differentiated pollution reduction and carbon mitigation coordination.

The accurate and rapid prediction of ticket prices for a public-private partnership (PPP) subway system, which is an important research topic in the field of civil engineering management, is of critical importance to ensure its smooth operation. To effectively cope with the effects of multiple influencing factors and strong nonlinearity among them, the mean impact value (MIV) method and the back-propagation (BP) feed-forward neural network improved by the sparrow search algorithm (SSA) are used in this study to develop an intelligent prediction model. First, we considered the relationship of the supply and the subway system service, which is a typical quasi-public product, and analyzed the relevant factors affecting its price adjustment. Then, we developed an intelligent method for the prediction of ticket prices based on the SSA-BP. This model not only makes full use of the powerful nonlinear modeling ability of the BP algorithm, but also takes advantage of the strong optimization ability and fast convergence speed of the SSA. Finally, this study screened out the key input factors by adopting the MIV method to simplify the structure of the BP algorithm and achieve a high prediction accuracy. In this study, Beijing Subway Line 4, Wuhan Metro Line 2, and Chengdu Metro Line 1 were selected as case study sites. The results showed that the linear correlations between influencing factors and ticket price for the PPP subway system service were weak, which indicated the need for using nonlinear analysis methods such as the BP algorithm. Compared with other prediction methods (the price adjustment method based on PPP contract, the traditional BP algorithm, the BP neural network improved by the genetic algorithm, the BP algorithm improved by the particle swarm optimization, and the support vector machine), the model proposed in this paper showed better prediction accuracy and calculation stability.

The development of a city needs the accumulation of culture, and historical buildings are the most direct witness of the rise and fall of a city. Like the human body, historical buildings have a certain life cycle, but the acceleration of urbanization and unreasonable use cause an irreversible reduction in the remaining life of historical buildings. There is a notable lack of quantitative analysis regarding the residual life of historical buildings. Therefore, identifying the factors that influence their residual life is crucial for both preserving these buildings and sustaining urban culture. In order to obtain a more accurate correlation degree of influencing factors, a systematic-analysis model of influencing factors on the residual life of historical buildings based on the entropy weight method (EWM) and the grey relation analysis method (GRA) was established, so as to excavate the mechanism of the influencing factors on the residual life of historical buildings, accurately identify the main influencing factors on the residual life of historical buildings, and propose preventive measures. The results show that the structural system has the greatest influence on the residual life of historical buildings, followed by the enclosure system, and the equipment system. The research findings offer valuable insights for extending the residual life of historical buildings in the future.

Aiming at the real-time observation requirements in marine science and ocean engineering, based on underwater acoustic communication and satellite communication technology, a seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO) is proposed. It consists of a seabed observation system, a sea surface relay transmission buoy, and a remote monitoring system. The system communication link is implemented by underwater acoustic communication and satellite communication. The seabed observation system adopts the “ARM + FPGA” architecture to meet the low power consumption, scalability, and versatility design requirements. As a long-term unattended system, a two-stage anti-crash mechanism, an automatic system fault isolation design, dual-medium data storage, and improved Modbus protocol are adopted to meet the system reliability requirements. Through the remote monitoring system, users can configure the system working mode, sensor parameters and acquire observation data on demand. The seabed observation system can realize the observation of different fields by carrying different sensors such as those based on marine engineering geology, chemistry, biology, and environment. Carrying resistivity and pore pressure sensors, the SRSS/ILMO powered by seawater batteries was used for a seabed engineering geology observation. The preliminary test results based on harbor environment show the effectiveness of the developed system.

The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method.

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

The unmanned surface vehicle (USV) integrated with acoustic modems has some advantages such as easy integration, rapid placement, and low cost, which becomes a kind of selective novel node in the underwater acoustic (UWA) communication network and a kind of underwater or overwater communication relay as well. However, it is difficult to ensure the communication quality among the nodes on the network due to the random underwater acoustic channel, the severe marine environment, and the complex mobile node system. Aiming to model the communication characteristics of the USV, the multipath effect and Doppler effect are main concerns for the UWA communication in this paper, so that the ray beam method is utilized, the channel transmission function and the channel gain are obtained, and the mobile communication quality evaluation model is built. The simulation and lake experiments verify that the built mobile UWA communication quality evaluation model on USV can provide preference and technique support for USV applications.