This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations are analyzed. Research has shown that using the RF+IML method to study the impact of weather variables on shared bicycle transfer volume is feasible. There is a significant nonlinear relationship between various weather factors and shared bicycle transfers. Temperature, humidity, and rainfall have specific activation and threshold effects on the number of shared bicycle transfers. When humidity is below 60%, the variation in transfer volume remains relatively stable; however, once it exceeds 60%, the transfer volume drops sharply. When the temperature exceeds 17 °C, its impact tends to reach saturation. Similarly, when rainfall reaches around 20 mm, its adverse effect also approaches the threshold. Temperature is the most important factor affecting the prediction of shared bicycle transfer volume, with temperature, cold weather, and cold forecasts contributing over 35% to the total effect. The interaction effect between temperature and other weather factors accounts for 22% of the total effect.

Due to the presence of numerous karst cavities, fractures, and fissure water, karst strata exhibit significant air leakage during pressurized chamber opening. This leads to a large gas replenishment volume, substantial formation water inflow, and the inability to conduct pressurized chamber opening operations normally. To address this issue, this study focuses on the shield tunneling project in the first standard section and second work area of the Shenzhen Metro Line 16 utility corridor. Based on the engineering characteristics of highly developed karst strata, the technical mechanism of the thick-slurry chamber-filling method for pressurized chamber opening is analyzed. The slurry mix design parameters, filling parameters, grouting parameters, working pressure for pressurized chamber opening, construction technology, and control measures are systematically summarized. Consequently, a chamber-opening technology using the thick-slurry chamber-filling method for Earth Pressure Balance (EPB) shield tunneling in karst formations is proposed. The results indicate that the thick-slurry chamber-filling method is an effective approach for chamber opening in EPB shield tunneling through karst formations. It can successfully mitigate issues related to formation air leakage during chamber opening, thereby ensuring operational safety, enhancing efficiency, and reducing costs.

During the long-term service of highway tunnel lighting systems, concurrent failures of multiple luminaires lead to severe deterioration of road surface illuminance distribution. Meanwhile, conventional periodic maintenance schemes suffer from significant time latency, failing to ensure driving safety during the maintenance latency period. To address this challenge, this study proposes a highway tunnel lighting quality optimization method based on luminous flux redistribution. First, based on the spatial geometric relationships of failed luminaires, multiple luminaire failure (MLF) scenarios are categorized into four typical topological structures: unilateral continuous failure (UCF), unilateral alternate failure (UAF), bilateral symmetric failure (BSF), and bilateral staggered failure (BStF). The degradation characteristics of average road surface illuminance and illuminance uniformity under each topology are quantitatively investigated. Second, with the optimization objectives of maximizing overall illuminance uniformity (U₀) and centerline longitudinal illuminance uniformity (U₁), a multi-dimensional optimization model is established. This model incorporates foundational constraints, including driving safety, physical dimming boundaries of luminaires, and road surface illuminance stability. Furthermore, to mitigate the transverse dark bands, lateral illuminance imbalance, and longitudinal fluctuations induced by MLF, four enhanced constraints are introduced: global minimum average illuminance, macroscopic regional symmetry, cross-sectional minimum illuminance, and longitudinal illuminance gradient limits. To efficiently solve this high-dimensional nonlinear optimization problem, a hybrid heuristic particle swarm optimization-differential evolution-simulated annealing (PSO-DE-SA) algorithm integrating PSO, DE, and SA mechanisms is developed. Finally, systematic experimental verifications are conducted across the four MLF topologies in a unidirectional three-lane tunnel scenario. The results demonstrate that for discrete failure scenarios supported by adjacent operational luminaires, the proposed method effectively mobilizes redundant luminous flux to achieve substantial restoration of lighting quality. Specifically, under UAF, the average road surface illuminance is restored to 153.46 lx, approaching the ideal non-failure state; under BStF, U₁ increases by 25.02%; under BSF, U₁ increases by 23.96%, effectively repairing transverse illuminance faults. However, under UCF involving three or more luminaires, the lighting system reaches its physical dimming compensation limit. Although the overall regional metrics can still satisfy the basic mandatory requirements, the illuminance in the specific lane with failed luminaires cannot be restored to the required standard safety level, leaving localized dark zones. This finding provides a quantitative criterion for determining the triggering threshold of preventive maintenance and luminaire replacement in tunnel operation and maintenance.

This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations are analyzed; Based on the spatial Durbin model, analyze the relationship between the transfer volume of shared bicycles and factors such as the built environment. Research has shown that there is a significant non-linear relationship between various weather factors and the transfer volume of shared bicycles; Temperature, humidity, and rainfall have specific activation and threshold effects on the number of shared bicycle transfers. Population statistics, socio-economic factors, building environment, road environment and other characteristic variables have a significant impact on the shared bicycle transfer volume in each study area and adjacent study areas under adverse weather conditions, indicating that factors such as built environment have a spatial spillover effect on the shared bicycle transfer volume.

This article explores the impact of weather and environment on shared bicycles. Using a random forest model combined with explanatory machine learning methods, the relationship, threshold effect, and interaction effect between weather factors and the transfer volume of shared bicycles at subway stations are analyzed; Research has shown that using the RF+IML method to study the impact of weather variables on shared bicycle transfer volume is feasible. There is a significant non-linear relationship between various weather factors and shared bicycle transfers. Temperature, humidity, and rainfall have specific activation and threshold effects on the number of shared bicycle transfers. When humidity is below 60%, the variation in transfer volume remains relatively stable; however, once it exceeds 60%, the transfer volume drops sharply. When the temperature exceeds 17 °C, its impact tends to reach saturation. Similarly, when rainfall reaches around 20 mm, its adverse effect also approaches the threshold. Temperature is the most important factor affecting the prediction of shared bicycle transfer volume, with temperature, cold weather, and cold forecasts contributing over 35% to the total effect. The interaction effect between temperature and other weather factors accounts for 22% of the total effect.During the long-term service of highway tunnel lighting systems, concurrent failures of multiple luminaires lead to severe deterioration of road surface illuminance distribution. Meanwhile, conventional periodic maintenance schemes suffer from significant time latency, failing to ensure driving safety during the maintenance latency period. To address this challenge, this study proposes a highway tunnel lighting quality optimization method based on luminous flux redistribution. First, based on the spatial geometric relationships of failed luminaires, multiple luminaire failure (MLF) scenarios are categorized into four typical topological structures: unilateral continuous failure (UCF), unilateral alternate failure (UAF), bilateral symmetric failure (BSF), and bilateral staggered failure (BStF). The degradation characteristics of average road surface illuminance and illuminance uniformity under each topology are quantitatively investigated. Second, with the optimization objectives of maximizing overall illuminance uniformity (U₀) and centerline longitudinal illuminance uniformity (U₁), a multi-dimensional optimization model is established. This model incorporates foundational constraints, including driving safety, physical dimming boundaries of luminaires, and road surface illuminance stability. Furthermore, to mitigate the transverse dark bands, lateral illuminance imbalance, and longitudinal fluctuations induced by MLF, four enhanced constraints are introduced: global minimum average illuminance, macroscopic regional symmetry, cross-sectional minimum illuminance, and longitudinal illuminance gradient limits. To efficiently solve this high-dimensional nonlinear optimization problem, a hybrid heuristic particle swarm optimization-differential evolution-simulated annealing (PSO-DE-SA) algorithm integrating PSO, DE, and SA mechanisms is developed. Finally, systematic experimental verifications are conducted across the four MLF topologies in a unidirectional three-lane tunnel scenario. The results demonstrate that for discrete failure scenarios supported by adjacent operational luminaires, the proposed method effectively mobilizes redundant luminous flux to achieve substantial restoration of lighting quality. Specifically, under UAF, the average road surface illuminance is restored to 153.46 lx, approaching the ideal non-failure state; under BStF, U₁ increases by 25.02%; under BSF, U₁ increases by 23.96%, effectively repairing transverse illuminance faults. However, under UCF involving three or more luminaires, the lighting system reaches its physical dimming compensation limit: the average road surface illuminance cannot be restored to the safety threshold, ensuring lighting quality only on the lane side with operational luminaires. This finding provides a quantitative criterion for determining the triggering threshold of preventive maintenance and luminaire replacement in tunnel operation and maintenance.