Using raw coal gangue (RCG) as a coarse aggregate in concrete mitigates the environmental issues of RCG and reduces the demand for natural gravel, providing a sustainable alternative for the construction industry. However, the high crushing index and porosity of raw coal gangue aggregate (RCGA) lead to degraded mechanical properties of concrete. This study systematically investigates the enhancement of raw coal gangue aggregate concrete (RCGAC) using polypropylene fibres. The effects of water–binder ratio, RCGA replacement ratio, and polypropylene fibre content on mechanical properties are examined. By analysing both macroscopic failure patterns and microscopic structural characteristics, the enhancement mechanisms of the polypropylene fibres are clarified. Furthermore, existing prediction models are evaluated against experimental data, and modified models are proposed for predicting the splitting tensile strength and elastic modulus of polypropylene fibre-reinforced RCGAC, with the RCGA replacement ratio incorporated as a variable. The results indicate that EC 2, ACI 318, and GB/T 50010 standards are inadequate for predicting these properties for the developed material. The modified prediction models integrate the RCGA replacement ratio as a variable and offer high accuracy and reliability for assessing the mechanical properties of polypropylene fibre-reinforced RCGAC.
China's rapidly expanding urban rail transit (URT) system faces significant environmental challenges, particularly regarding the inflexibility and structural nature of passengers’ urban transportation demands. It is a key issue to assess the carbon peak timeline and related technological roadmap for carbon peaking in China’s URT sector. This study conducts a comprehensive life cycle assessment to evaluate carbon dioxide emissions across different URT life cycle phases. Through input factor identification and scenario modelling, the authors investigate critical factors affecting life cycle emissions and project emission data from 2019 to 2035. The analysis results suggest that there was an 11% reduction in emissions in 2020, which is attributed to the shifts in transportation demand induced by COVID-19. Among various development scenarios, only the ambitious scenario, which involves the adoption of decarbonisation technologies and robust policy enforcement, achieves carbon peaking by 2030. To support emission management, the authors propose a multi-level framework that combines upstream data integration with goal decomposition methodology. This framework is based on macro-level sectoral assessments and the development of technology-specific roadmaps, thereby supporting the sustainability of the URT system in line with China's national climate objectives while maintaining transportation efficiency.
This study applies a system dynamics modelling framework to assess long-term sustainability strategies for urban environments across four policy scenarios, focusing on energy production, land use, and consumption behaviour. Scenario 1 simulates the expansion of solar infrastructure to meet rising energy demands, resulting in significant carbon dioxide emission reductions and improved quality-of-life indicators – consistent with prior research on renewable energy benefits. Scenario 2 evaluates regulatory interventions aimed at conserving green spaces and controlling construction activities. The outcomes include increased vegetation cover and a notable decline in urban pollution, supporting evidence from urban ecological planning studies. Scenario 3 integrates the first two strategies, yielding optimal results in reducing emissions and enhancing livability; however, these improvements also attract higher population inflows, increasing energy demand. To address this challenge, Scenario 4 introduces a targeted energy efficiency strategy, incorporating per-capita consumption controls through technological advancements and behavioural change mechanisms. This scenario achieves a 35% reduction in total energy use and a 25% decline in carbon dioxide emissions. The findings highlight the synergistic potential of combined policy approaches and underscore the importance of managing rebound effects associated with urban quality improvements.
The engineering of sustainable and resilient evacuation systems requires behavioural models that identify who is likely to evacuate and which barriers constrain protective action. Using stated-preference survey data from 725 residents in the New Madrid Seismic Zone, this study compares mixed logit, extreme gradient boosting (XGBoost), and Bayesian Additive Regression Trees () for earthquake evacuation decisions. The sample showed a high stated intention to evacuate (79.3%), indicating both strong perceived risk and a potential for hypothetical bias. XGBoost achieved the best predictive performance ( = 0.808, accuracy = 76.1%, F1-score = 0.828), followed by ( = 0.792) and mixed logit ( = 0.744). The main innovation is a triangulated framework that combines econometric interpretation, interpretable machine learning through SHAP, and Bayesian uncertainty quantification. Across all models, budget availability, dwelling type, and information-seeking behaviour emerged as the most influential predictors. These results provide practical evidence for socially sustainable evacuation planning, including targeted financial assistance, communication strategies for single-family residential areas, and smart information systems that support equitable disaster response.
Steel slag is an industrial solid waste that causes serious environmental hazards when stockpiled without treatment. Existing research mainly addresses static corrosion of steel-slag concrete, whose conclusions cannot be directly extended to coastal zones, where dry-wet cycles modify degradation mechanisms through repeated salt dissolution-crystallisation. Moreover, systematic investigations into preferred mix proportions of water-binder ratio and steel slag content for chloride and sulfate dry-wet attack separately remain scarce. This work explores steel slag-reinforced cement mortar resistance under 5% sodium chloride (NaCl) and 5% sodium sulfate (Na-2 SO4 ) dry-wet cycles (water- binder ratio: 0.3-0.5 and steel slag content: 0-20 wt.% of binder). Under sodium chloride cycling, specimens with 10% steel slag and 0.4 water-binder ratio achieved optimum comprehensive performance at 20 cycles: mass loss rate, loss of relative dynamic modulus of elasticity, electrical resistance decrease rate and steel corrosion rate dropped by 73%, 82%, 76% and 47%, respectively. For sodium sulfate exposure, specimens with 15% steel slag and 0.4 water-binder ratio yielded the best performance. Multi-scale micro-characterisation (scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy and X-ray diffraction) reveals that steel slag suppresses corrosion product genera-tion. This study proposes favourable mix proportions for this material under the test conditions and offers experimen-tal support for the utilisation of steel slag in coastal engineering.
