The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental influences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research findings revealed that two options can be considered when combining SD with ABM; the two options are discussed.

With the recent fast economy development and rapid urbanization, the huge generation of construction waste has become a threat to sustainable development in China. Though efforts have been made to promote reuse and recycling of construction waste, landfilling of waste remains the most commonly adapted approach for construction waste disposal. As the space for landfills is limited and because of the negative issues in terms of environmental and social aspects that may be caused, the appropriate site selection of landfills is crucial. With this background, this paper aims to establish a framework for facilitating landfill selection for construction waste. To begin with, a total of sixteen factors that may influence landfill site selection were identified from a literature review. Then, based on the combined analytic hierarchy process (AHP) and entropy method, the weights and the final comprehensive scores of the identified factors were calculated. According to the derived results, potential sites for landfills were divided into three levels, namely the most appropriate (0.38%), appropriate (17.58%), and inappropriate (82.04%). The proposed decision-making methods in this paper can provide a valuable reference for the selection of construction waste landfill sites.

A novel 3D printing material based on hydroxypropyl methylcellulose (HPMC)—improved sulphoaluminate cement (SAC) for rapid 3D construction printing application is reported. The hydration heat, setting time, fluidity of paste and mortar, shape retainability, and compressive strength of extruded SAC mortar were investigated. HPMC dosage, water-to-cement (W/C) ratio, and sand-to-cement (S/C) ratio were studied as the experimental parameters. Hydration heat results reveal HPMC could delay the hydration of SAC. The initial and final setting time measured using Vicat needle would be shortened in the case of W/C ratio of 0.3 and 0.35 with HPMC dosage from 0.5% to 1.5%, W/C ratio of 0.40 with HPMC dosage of 0.5%, 0.75%, and 1.5%, and W/C ratio of 0.45 with HPMC dosage of 0.45, or be extended in the case of W/C ratio of 0.4 with HPMC dosage of 1.0% and W/C ratio of 0.45 with HPMC dosage from 0.75% to 1.5%. Fluidity measurement shows HPMC significantly improves the shape retainability. Furthermore, the addition of HPMC remarkably increased the compressive strength of extruded mortar. The results showed that HPMC could be used to prepare 3D printing SAC having satisfactory shape retainability, setting time and compressive strength.

Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systematically studied. The research is carried out using a co-simulation test platform integrating TRNSYS and Python. Results show that the XGBoost model achieves the highest prediction accuracy. LightGBM model’s accuracy is marginally lower but requires significantly less time for both prediction and training. In this research, the proposed control strategy decreases the economic cost by 21.61% compared to the baseline case under traditional control, with the weighted indoor temperature rising by only 0.10 K. The result also suggests that it is worth exploring advanced prediction models to increase prediction accuracy, even within the high prediction accuracy range. Furthermore, implementing MPC control for demand response remains beneficial even when the model prediction accuracy is relatively low.

The intensification of global climate change has exacerbated building energy consumption issues, presenting a significant challenge in retrofitting existing buildings to meet current environmental requirements while adapting to future climate scenarios. A multi-objective optimization design process for building envelope retrofits was developed in this study, utilizing random forest (RF) models and atmospheric circulation models to generate future weather data. Building Information Modeling (BIM) and performance simulations were employed to obtain data under various renovation scenarios. A BP neural network model related envelope design parameters to performance indicators, while the NSGA-III algorithm optimized retrofit strategies. The proposed method demonstrates improved efficiency and validity in developing energy-saving solutions that consider future climate scenarios. Results highlight the importance of incorporating climate change factors in retrofit designs, providing valuable insights and methodological support for decision-makers in building energy conservation practices.