Abstract:

The structural degradation characteristics of expansive claystone have led to engineering problems that have attracted widespread attention both domestically and internationally. This paper reviews the significant achievements currently obtained in the macroscopic and microscopic characterization， degradation mechanisms， and modeling of the structural degradation of expansive claystone under different hydro-mechanical conditions. The experimental results are classified and summarized into macroscopic indicators （physical and mechanical properties） and microscopic indicators （qualitative analysis and quantitative indicators）. The degradation mechanisms are clarified and summarized as hydro-induced degradation and force-induced degradation. The methods for degradation models are summarized and refined into empirical correction methods， elastoplastic damage methods， and structural simplification methods. To address unsolved problems， this paper provides specific suggestions and future research directions from the perspective of improving the evaluation indicators， deepening the mechanisms， and establishing models for the structural degradation of expansive claystone， in order to provide references and insights for practical engineering applications and research of claystone-containing projects.

Abstract:

Five centrifugal model tests were conducted to investigate the deformation characteristics of geosynthetics reinforced soil （GRS） abutment under service conditions. The setback distance （i.e.， the distance from the beam seat to the inner of the facing） and beam seat width were considered as two influencing factors. The GRS abutment was loaded through multi-stage loading in centrifugal field. Test results indicate that the deformation of the GRS abutment is closely related to the setback distance and the beam seat width. The deformation decrease as the setback distance increases， there exists an optimal setback distance， beyond which the effect on controlling the abutment deformation becomes minimal. The deformation increase as the beam seat width increases under the same load. In addition， the maximum reinforcement strain decreases as the setback distance increases， and increases as the beam seat width increases. At each reinforcement layer， the maximum strain occurs below the beam seat and gradually transitions toward the setback area along the facing. Additionally， the peak strain at each reinforcement layer also decreases from top to bottom. The volumetric strain of GRS abutments in all tests is less than 0.15%， and the assumption of “zero volumetric strain” can still be used for GRS abutments. There is a significant deviation between the calculated values of the maximum horizontal displacement given by the existing specification and the measured values， which cannot well reflect the influence of setback distance and beam seat width， indicating the need for further optimization. The results can provide references and basis for the design of GRS abutments and the formulation of relevant specifications.

Abstract:

To facilitate the promotion and application of prefabricated retaining walls， this paper analyses current research and application status both domestically and internationally， and proposes a prefabricated cantilever retaining wall that utilizes combined connections using socket and bolt. The overall configuration， structural calculation， and installation process are studied， and three aspects including theoretical analysis， experimental verification， and engineering application are discussed. The proposed prefabricated cantilever retaining wall meets structural safety requirements while offering fast and convenient prefabrication and installation， and has significant value for widespread application.

Abstract:

Individuals who spend long periods of time in confined spaces that lack light are prone to problems such as circadian rhythm disruption and sleep disorders. This paper investigates the impact of lighting on 20 subjects in an underground confined space over 4 consecutive weeks， with different lighting pattern applied each week. Melatonin， cortisol， subjective sleepiness， and core body temperature are measured at bedtime， while metrics such as subjective sleep scores， sleep onset latency， nocturnal reaction time， and error rate are monitored. The results show that a daily decrease in melatonin concentration and sleepiness at bedtime and a daily increase in core body temperature under the constant lighting pattern in the 1st week. In the 2nd week， the routine is advanced by 2 hours. In the 3rd week， a dynamic lighting pattern with bright light stimulation at night led to an increase in core body temperature and cortisol concentration at bedtime， a decrease in melatonin concentration and subjective sleepiness， as well as a daily increase in sleep scores and sleep latency. Additionally， sleep quality deteriorated daily， with shorter reaction times and lower error rates. In the 4th week， a dynamic lighting pattern of strong lighting stimulation in the morning and dim light protection at night resulted in a significant increase in melatonin concentration and sleepiness at bedtime， as well as a day-by-day decrease in core body temperature and sleep latency. Sleep quality gradually improved over the week. These findings suggest that the effects of lighting on circadian rhythms and sleep quality cumulate over time. Dynamic lighting patterns can be strategically used to help people in the confined spaces maintain stable circadian rhythms and adapt to respective shift work schedules.

Abstract:

This paper aims to overcome the limitations of existing research that simplifies the traffic congestion source-tracing problem into path flow estimation or congestion correlation analysis. It proposes a more comprehensive and effective system for tracing vehicle paths in traffic-congested sections of urban expressways. Using the path as the basic analysis unit， it develops an innovative unified framework integrating both path flow estimation and congestion correlation analysis. Additionally， it proposes a method based on the route-based deformable convolution long short-term memory neural network （RSDC-LSTM）. The model consists of three core modules： constructing a path state feature set based on historical path flow data and short-term prediction data； quantifying the dynamic influence weights of each path on traffic congestion through a collaborative modeling of the multi-path convolutional long short-term memory network and the soft-attention mechanism； and using the deformable convolutional neural network to capture the spatial-topological correlation features of congested sections and achieve the evaluation of path importance in both spatial and temporal dimensions. Empirical research shows that RSDC-LSTM effectively identifies key paths of traffic congestion and ranks their influence. By regulating the top 10% of high-influence paths， peak travel speeds can be increased by 23.36%， while the number of stops and delay time can be reduced by up to 29.41% and 43.82% respectively. The RSDC-LSTM method proposed provides a quantifiable decision-making framework for developing dynamic traffic control strategies and contributes to improving the traffic operation efficiency of urban expressways.

Abstract:

Unprotected turns account for efficiency loss and even accidents for autonomous vehicles at intersections. Exploring the consistency direction and evolution process of human driver intentions in this scenario can expedite the achievement of mutual intention consistency between autonomous and human-driven vehicles， thereby enhancing safety and efficiency. This paper aims to explore the orientation of intention consistency and evolution of human driver intentions in unprotected left-turns to accelerate the mutual intention consistency between autonomous and human-driven vehicles. First， based on the XXJH dataset from the Xianxiajian Intersection in Shanghai， China， and the inD dataset from Germany， empirical trajectories of unprotected left-turn scenarios were extracted. Then， based on the principle of empathy， an indicator named cooperative acceleration was proposed to represent real-time intent inclination. The relationship between cooperative acceleration， time to conflict point （T）， and unilaterally expected consistency direction was modeled using support vector machines. Furthermore， to explore the bilateral expected consistency orientation and its variation process， the aforementioned functions were mapped onto a coordinate space composed of the time to conflict point for left-turning vehicle and the opposite straight-going vehicles （i.e.， Tl and Ts）. An analysis method called time-domain-based decision diagram was proposed. By mining the evolution process of human driver intentions in empirical data， the conflicting region of expected intentions （referred to as the “dilemma zone” in left-turn scenarios） was identified. In the inD dataset， the dilemma zone was located above the line Ts=Tl, while in the XXJH dataset， the overall distribution of the expected conflict zone was near this line. It indicated from the perspective of intent decision and evolution that straight-going traffic had higher priority in the inD dataset， while in the XXJH dataset， both left-turn and opposing straight-going traffic considered their priority to be roughly equal. The confidence time domain for consistency were mined， i.e.， the interaction state was in an area where the time to conflicting point was less than 2s and the convergence ratio was on average above 95%. Finally， this paper treated the results of intention recognition and consistency evolution as a priori knowledge to the interaction strategy of autonomous vehicles and discussed the intended cooperative decision and planning.

Abstract:

To investigate the appropriate length of the auxiliary lane at a two-lane highway entrance， this study utilizes roadside LiDAR to collect data on vehicle operating speeds and headway distributions within the auxiliary lane area and analyzes their distribution characteristics. Additionally， UAV aerial videos combined with Tracker vehicle tracking software are employed to obtain accepted and rejected gaps at the entrance auxiliary lane section. Using the Raff critical gap estimation method， the critical gap for this section is determined to be 2.475 s. Based on driver behavior in the entrance auxiliary lane section， as well as vehicle operation characteristics and microscopic lane-changing behaviors， a length calculation model for the two-lane entrance auxiliary lane is established. The auxiliary lane is divided into two components： waiting distance and left lane-changing distance. The model parameters are calibrated based on the critical gap， vehicle speed， and headway distribution characteristics， and a recommended length for the two-lane entrance auxiliary lane is proposed. The results indicate that the length of the entrance auxiliary lane is positively correlated with vehicle speed. Moreover， the recommended auxiliary lane length proposed in this study is shorter than the value specified in the Design Specification for Highway Alignment， suggesting that the existing standard provides a certain length margin. In land-constrained areas， vehicle operating speed can be regulated to appropriately shorten the auxiliary lane length， optimizing land use efficiency.

Abstract:

A non-contact detection algorithm based on vision sensors is proposed to address the issue of track area segmentation in the context of fully automatic driverless trains for rail transit. The algorithm uses frame difference threshold and grayscale distribution feature extraction methods to perform scene recognition and labeling of video image data. Image preprocessing and edge detection of the track contour are completed by an adaptive edge detection module， which adjusts the parameter input based on the results of image scene recognition. The track area boundary search module consists of two submodules： the sliding pane search submodule and the passband search submodule， based on the sliding pane-like approach， in order to extract the track outline curve. Finally， a Kalman filter is used to improve the accuracy and robustness of the detection results. The experimental results show that the algorithm exhibits strong detection performance on track boundaries.

Abstract:

The AB skip-stop operation can meet the rapid travel demand of passengers in the scenario of commuting metro lines during peak hours. Considering passenger transfer convenience and waiting safety， a 0-1 integer programming model of AB skip-stop operation for the metro system was developed based on travel process and transfer type， to minimize the total travel time of passengers while considering the fairness of transfer passengers. Additionally， an efficient variable neighborhood search algorithm was designed to solve this model. Taking Shanghai Metro Line 11 as an example， the effectiveness of the model and algorithm was verified. The results show that the variable neighborhood search algorithm provides better solutions in a short time compared to the genetic algorithm and can be effectively applied to the optimization model of the AB skip-stop operation. Under the AB skip-stop operation， the average travel time of passengers is reduced by 2.91 minutes. When considering the artificial experience strategy under the AB skip-stop operation， the average travel time is reduced by 2.12 minutes， and the number of transfer passengers decreases by 41.18 %. Sensitivity analysis reveals that the penalty coefficient for transfer station waiting time， train departure interval， and maximum AB station interval are the key factors affecting the optimization results.

Abstract:

To address issues such as the unclear sequence of procedures and ambiguity in the personnel responsible for executing the emergency response procedures in text-based metro emergency response processes， this paper proposes a knowledge extraction and knowledge reasoning method for metro emergency response procedures based on knowledge graph of bidirectional long short-term memory- conditional random field （BiLSTM-CRF）. First， the BiLSTM-CRF method is used to identify the named entity of the text data of the metro emergency response process， and complete the knowledge extraction of the text data. Then， the TransD model is selected to conduct knowledge inference on the identified entity data， thereby completing the construction of a knowledge graph with entities and attribute pairs as nodes and relational pairs as edges. Finally， the Neo4j graph database is used to visualize and analyze the knowledge graph of metro emergency response process. The research results show that the precision， recall， and F1 value of the knowledge extraction model based on BiLSTM-CRF have all reached more than 90%， and the accuracy of the inference results of the TransD model based on BiLSTM-CRF has increased by 22.92%， ensuring the accuracy of knowledge graph construction and providing decision support for subway emergency management.

Abstract:

To address the issue of the safe operation of long combined trains， the existing intelligent braking test platform of 150 marshaling vehicles were effectively utilized to predict the braking performance of ultra-long trains. An accurate predictive model was established for changes in train tube pressure in large combinations of vehicles with different grouping positions. A pressure difference model of the train pipe was proposed， a simulation mathematical model was developed using the curve fitting method and theoretical deduction， and an improvement method was suggested for the theoretical pressure difference model of the train pipe. Additionally， a train tube pressure change prediction model was obtained by superimposing the train tube pressure difference model and the first train tube pressure change model. The results indicate that the error is maintained within 10%. Finally， the change curve of train pipe pressure was obtained under the braking action of 200 assembled trains using this prediction model.

Abstract:

The planar laser induced fluorescence （PLIF） method was employed to investigate the mass exchange characteristics of water in floating photovoltaic modules. The impact of temperature difference， ambient flow velocity， and perforation rate were observed. The experimental results indicate that the dimensionless concentration of the original warm water in the module decreases exponentially over time， with the retention duration determined by the combined effects of temperature difference， flow velocity， and the perforation rate. Under small temperature differences， flow velocity has a significant impact on the mass exchange， while an increased perforation rate is more effective under large temperature differences. In addition to providing direct diluting effects， the water flow from the punching area was observed to enhance the large-scale circulations in the turbulent layer at the bottom of the base module， resulting in a significant improvement in overall mass exchange efficiency.

Abstract:

Due to the optical complexity of water bodies and the interactions among various water quality parameters， utilizing ensemble machine learning methods for estimating water quality parameters offers advantages. However， selecting hyperparameters in the modeling process remains challenging. The sparrow search algorithm （SSA） can rapidly search for optimal parameters of ensemble machine learning models， while the Levy flight algorithm prevents SSA from being trapped in local optima， thereby improving the accuracy and efficiency of the model. In this paper， the Levy flight algorithm and SSA were used to optimize three ensemble learning models： random forest （RF）， AdaBoost regression （ABR）， and CatBoost regression （CBR）. Taking Zhengzhou Dongfeng Canal and Xiong’er River as the study area， estimation models （LSSA-RF， LSSA-ABR， and LSSA-CBR） were developed based on measured chlorophyll-a and total suspended solids concentrations. The experimental results show that after optimization， various indicators show improvements to varying degrees. Among them， the LSSA-CBR model exhibits the best performance. The CBR model， which is modeled under the gradient boosting framework， demonstrates higher learning capability compared to RF and ABR models. For the estimation of chlorophyll-a， the root mean square error （RMSE） of the LSSA-CBR estimation model is 2.325 μg·L^-1， and the coefficient of determination （R²） is 0.896. For the estimation of total suspended solids， the RMSE of the LSSA-CBR model is 1.598 mg·L^-1， and R² is 0.882. Finally， the LSSA-CBR model， demonstrating strong accuracy， was applied to Planet images to evaluate the spatial distribution of chlorophyll-a and total suspended solids in rivers， providing a valuable reference for quickly understanding the distribution of urban river water quality and conducting water quality assessment and management.

Abstract:

To address the influence of water molecules participating in the crosslinking reaction on the crosslinking process and microstructure characteristics of polymer grouting material in watery environments， cross-linked polyurethane models with different water molecule mass fractions were constructed and molecular dynamics simulations were performed to calculate the number of water molecules participating in the crosslinking reaction， glass transition temperature， and free volume of systems with different water molecule mass fractions. Additionally， a method for calculating the uniformity of crosslinking distribution was proposed. The results show that as the water content increases， the number of water molecules participating in the crosslinking reaction first gradually increase and then stabilize， because the volume of water molecule clusters increases with the increase of water molecule mass fraction， which limits the participation of water molecules in crosslinking reaction. With higher water content， the glass transition temperature of cross-linked polyurethane is lower and the free volume is smaller. Water molecules participating in the crosslinking reaction improve the uniformity of crosslinking distribution in the model. As the water content increases， the crosslinking distribution of the model becomes more uniform. The current results will provide valuable theoretical reference for understanding the reaction process and evaluating the performance of polymer grouting materials in watery environments， as well as for the design and development of these materials in engineering applications.

Abstract:

The skill structure of human resources can significantly affect the efficiency of production lines， and the knowledge diffusion resulting from in-line apprenticeship training is one way to improve this structure. To describe the knowledge diffusion phenomenon， a social network is introduced， and a multi-skilled resource-constrained project scheduling problem is proposed considering the improvement of resource and skill efficiency under knowledge diffusion， assuming a known target human resource structure. A mixed integer programming model is developed to minimize both the number of projects required to achieve training objectives and the average cost required per project. An improved multi-objective evolutionary algorithm based on decomposition is designed to address this model， incorporating a dynamic programming resource allocation operator and a task clustering greedy schedule generation scheme. The superiority of the algorithm is demonstrated through numerical experiments.

Abstract:

To alleviate the pressure on the power grid， a method is proposed for aggregating electric vehicles （EVs） in grid dispatch. The method establishes a framework for grid-charging aggregation， incorporating the power grid， charging aggregators， and EVs. It utilizes a hybrid incentive approach that combines dynamic integration of incentives and time-of-use pricing to motivate user engagement. Real-time evaluation of user participation intention is conducted using fuzzy reasoning. Subsequently， a multi-objective optimization model is developed and validated using operational data from vehicles. The results demonstrate the superiority of the proposed method over alternative approaches， as it enhances grid charging capacity， reduces average electricity costs， and addresses low-demand periods， while efficiently catering to various vehicle-station scale scenarios.

Abstract:

This paper addresses the issue that inter-frequency clock bias （IFCB） affects the performance of triple-frequency undifferenced and uncombined precise point positioning ambiguity resolution （PPP-AR）. It proposes an epoch-differenced （ED） method to estimate IFCB for Global Positioning System （GPS）， Galileo Navigation Satellite System （Galileo）， and BeiDou Navigation Satellite System （BDS） satellites. It analyzes the time-varying characteristics of phase-specific IFCB （PIFCB） for these three satellite systems and evaluated the impacts of IFCB correction on extra-wide lane （EWL） uncalibrated phase delay （UPD） estimation for GPS Block IIF satellites and the performance of triple-frequency undifferenced and uncombined PPP-AR. The results demonstrate that the peak values of time-varying PIFCB series for GPS， Galileo， and BDS satellites are approximately 10 cm， 3 cm， and 5 cm， respectively. IFCB correction significantly improves the stability of EWL UPD estimation for GPS Block IIF satellites and enhances the performance of triple-frequency undifferenced and uncombined PPP-AR.

Abstract:

To explore the feasibility of horizontal well technology in in-situ leaching （ISL） of sandstone-type uranium mines in China， it is essential to analyze the hydrodynamic processes and quantitatively characterize the leaching range. The hydrodynamic processes of groundwater in the horizontal well ISL involve well-reservoir coupling simulation， which refers to the coupling of wellbore turbulence and reservoir Darcy flow. By developing numerical simulation techniques for well-reservoir coupling， constructing a groundwater flow model for uranium leaching in the horizontal well network， and creating automatic extraction techniques for the leaching range using particle tracking simulation with MODPATH and the Alpha-shape algorithm， the simulation and characterization of the flow state of the solution and the leaching range during ISL can be achieved. The research results indicate that， compared to the traditional MODFLOW model， the well-reservoir coupling model more accurately depict the interaction flow rate and its variations between the well and reservoir during the injection process in the horizontal well， and the interaction flow rate is positively correlated with the permeability coefficient （K） of the reservoir. Based on the well-reservoir coupling simulation， the range enveloping the particle trajectories captured by the pumping wells during the simulation period can be automatically extracted. By identifying the leaching range corresponding to the flow rate contribution of pumping wells， it is determined that the leaching range corresponding to a 95% flow rate contribution is 40% of the volume corresponding to a 100% flow rate contribution. This indicates that the flow interaction， from the injection of leaching solution from the horizontal well into the reservoir to the extraction of pregnant solution by vertical wells， is primarily concentrated within the leaching range in the early stage of ISL. The flow velocity along the outer trajectories is lower， resulting in a reduced contribution to both the pumping flow rate and the leaching rate.