A comprehensive water-energy-carbon (WEC) nexus model for an urban water system (UWS) using system dynamics is proposed to assist municipalities, urban developers, and policy makers for neighborhood water planning and management. The proposed model and decision support tool was developed for the operational phase of UWSs. The model was validated using historical water and energy consumption data (2005–2014) of Penticton (British Columbia, Canada). Spearman’s correlation coefficients between water and energy, water and carbon, and energy and carbon were 0.94, 0.89, and 0.83, respectively, revealing highly significant interconnections. The energy for water was $11.1\mathrm{MWh}/\mathrm{ML}$ , water for energy was $\mathrm{6,512}\mathrm{L}/\mathrm{MWh}$ , and carbon emissions were $124.4\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{MWh}$ from energy use and $120.8\mathrm{kg}{\mathrm{CO}}_2\mathrm{e}/\mathrm{ML}$ from wastewater processes. A Monte Carlo–based sensitivity analysis showed residential outdoor irrigation and water heating energy for showers and dishwashers have higher contribution to model variability. The intervention analysis reveals significant differences in savings in water, energy, and carbon for various water and energy-based interventions in UWSs and the developed tool is well capable for analyzing these dynamic savings.

Building information modeling (BIM) is an emerging tool in architecture/engineering/construction (A/E/C) industry that is used to design, document, and enhance communication among all the project stakeholders. Trim loss of rebar can be minimized with the use of discrete bars. To achieve this goal, a model to analyze reinforced concrete structure with one-dimensional (1D) cutting waste-optimization technique, integrated with BIM, is proposed. Building information modeling is selected as the hub in communicating project information among diverse design teams. This process permits project teams to utilize BIM models to simulate architectural and structural design requirements, and compare results speedily to make necessary changes in the designs to minimize rebar waste. The BIM rebar optimization analysis approach also supports cost-effective decision making during the design process. The proposed approach was validated with a two-story reinforced concrete structure, and the results indicated a high ...

The construction industry is continuously searching for sustainable materials to combat the rapid depletion of global resources and ongoing ecological crises. Biocomposites have recently received global attention in various industries due to their renewability, low cost, and biodegradability. Biocomposites’ potential as a sustainable substitute in construction can be understood by identifying their diverse applications. Moreover, examining their life cycle environmental and economic impacts is important. Therefore, this study is a novel attempt to encompass biocomposites’ construction applications and their environmental life cycle performance. Statistical analysis is done related to the temporal distribution of papers, publishers, literature type and regions of studies. First, this paper reviews the latest research on the applications of natural fiber biocomposites in construction with their key findings. The applications include fiber reinforcements in concrete, external strengthening elements, internally filled hollow tubes, wood replacement boards, insulation, and non-structural members. The second part covers the life cycle assessment (LCA) and cost studies on biocomposites. The life cycle studies are currently rare and require more case-specific assessments; however, they highlight the benefits of biocomposites in cost savings and environmental protection. Finally, this study provides key suggestions for increasing the applicability of biocomposites as sustainable construction materials.

Assessing the energy performance of existing residential buildings (ERB) has been identified as key to improving building energy efficiency and reducing associated greenhouse gas emissions in Canada. However, identifying optimal retrofit packages requires a significant amount of knowledge of building energy modelling, and it is a time-consuming and laborious process. This paper proposed a data-driven framework that combines machine learning, multi-objective optimization, and multi-criteria decision-making techniques to evaluate the energy performance of ERB and thereby formulate optimal retrofit plans. First, an artificial neural network (ANN) was developed to predict the energy performance of a wide range of retrofit packages. A genetic algorithm was employed to determine the best structure and hyperparameters of the ANN model. Then, the energy consumption results were integrated with environmental and economic impact data to evaluate the environmental and economic performance of retrofit packages and thereby identify Pareto optimal solutions. Finally, a multi-criteria decision-making method was used to select the best retrofit packages among the optimal solutions. The proposed framework was validated using data on a typical residential building in British Columbia, Canada. The results indicated that this framework could effectively predict building energy performance and help decision-makers to make an optimal decision when choosing retrofit packages.

Change iterations in construction create uncertainties and make the project management dynamic and unstable. Published literature shows that 30% of the construction cost is attributed to rework. Moreover, change orders are considered as the largest source of construction waste. Construction professionals have to make change decisions based on their previous experience and assumptions supported by incomplete information. Objective methods based on state-of-the-art technology can be systematically used to address the above challenge. Despite its promise, Building Information Modeling (BIM) based dynamic modeling has not been researched to estimate waste generation from change orders. In this paper, Building Information Modeling (BIM) was used to simulate the field data over the project lifecycle to analyze the ripple effects of construction changes. A system dynamic model was developed to map interactions in the system. Virtual BIM data was used to analyze the dynamic behavior of the variables due to the changes in the scope of work. Case study results showed that the client's commitment to decrease waste in the pre-project planning stage, with coordinated BIM design efforts, could reduce construction waste by up to 25%.