Muhammed Ali Bhuiyan

The use of municipal solid waste incineration (MSWI) ash as an alternative to conventional soil stabilisers offers an opportunity to promote circular economy practices. However, the environmental benefits of using waste-derived materials may be affected by additional processing and chemical inputs. This study calculates the carbon footprint of MSWI ash-based geopolymer and relevant uncertainties of design components using a comparative life cycle assessment (LCA). The functional unit is defined as the stabilisation of a 1 km-long road subgrade, with a width of 7 m and a stabilised layer thickness of 0.3 m, assuming a soil density of 1.8 tonnes/m³. Conducted LCA expands beyond the conventional system boundary by considering four scenarios: (1) direct environmental impacts, (2) benefits avoiding landfill diversion, (3) resource conservation avoiding virgin materials extraction and (4) combined credits from avoiding landfills and materials extractions. The assessment considers the production of alkali activators, MSWI ash processing, and the use of supplementary precursors, including ground granulated blast furnace slag (GGBFS) and kaolin. Life cycle impact assessment (LCIA) shows that the replacement of conventional stabiliser with MSWI ash contributes to a significant greenhouse gas emission. On the contrary, a higher emission has been observed for geopolymer production due to the additional chemical inputs but gained satisfactory strength. Landfill diversion of waste alone is insufficient to offset the emissions associated with hydrated lime. However, the environmental benefits become more evident when the avoidance of limestone extraction is included in the assessment. Correlation analysis further shows that sodium silicate (Na₂SiO₃) has a strong positive relationship with the total carbon footprint (r ≥ 0.80). Sodium hydroxide (NaOH) also shows a moderate relationship, with Pearson and rank correlation coefficients of 0.569 and 0.551, respectively. These results highlight the importance of alkali activators in determining the overall carbon footprint of geopolymer production. Critically, the environmental credits associated with landfill diversion and resource conservation are necessary to reflect the actual emission reduction strategies. Overall, the novelty of this research lies in diverting waste from landfills not necessarily translated into substantial carbon reductions. Instead, the environmental performance of waste-derived construction materials needs to be assessed by considering both their production impacts and the resources they help conserve.

This study explores the relationship between Artificial Intelligence (AI) and net-zero carbon buildings (NZCBs) and net-zero energy buildings (NZEBs) over the last decade. A thematic evolution has been observed in this research area, shifting from conventional optimization towards more advanced digital, intelligent, and decarbonized infrastructure. Co-occurrence, clustering, thematic evolution, network, and visualization justify this science mapping analysis at regular intervals (2015–2018, 2019–2022, and 2023–2026). Digital twins (DTs) have been identified as the dominant theme in strategic analysis, integrating Building Information Modeling (BIM), sensors, communication networks, and AI algorithms. In contrast, there has been the emergence of GIS as a complementary platform for extending AI applications beyond individual buildings to neighborhood, city, and regional scales through carbon mapping, life-cycle assessment, energy storage planning, and spatial decision-making. The analysis highlights AI as supporting technology rather than an isolated research theme, managing building information through digital twins and facilitating urban-scale decarbonization through GIS. The novelty of this study lies in proposing a dual framework that aligns digital twins and GIS as complementary implementation platforms for connecting AI with net-zero building objectives. The developed framework provides valuable insights into the intellectual structures creating intelligent, energy-efficient, and carbon-neutral built environments.

Residential building retrofit requires reliable building energy simulation and efficient performance prediction. This research presents an adaptable workflow for establishing two residential base model templates, namely corner space (CS) and intermediate space (IS). The models were assessed through National Construction Code of Australia-based verification, comparison with a documented Melbourne residential reference model, and Morris index convergence analysis. These models were coupled with an AI-assisted three-stage prediction framework and evaluated across seven additional Australian climates, with Melbourne as the primary case, to examine cross-climate applicability. Stage 1 developed a two-variable analytical model to predict annual energy consumption. Stage 2 extended this to a three-variable relation and compared its energy predictions with support vector machine (SVM). Stage 3 examined multivariable cases using four machine learning (ML) models—artificial neural network (ANN), SVM, random forest (RF), and decision tree (DT)—to predict performance metrics and identify the best model. For the primary Melbourne case, the two-variable relation reproduced the simulated response with mean absolute percentage errors (MAPEs) of 0.27% and 0.62% for CS and IS, respectively. In Stage 2, the analytical model achieved a MAPE of 8.38%, while SVM reduced the error to 0.75%. Cross-climate results confirmed that the staged workflow remained robust across different climates. In Stage 3, SVM was robust with limited data, while the ANN and RF became more competitive with larger samples. DT was prone to overfitting. The proposed framework supports early-stage residential retrofit assessment by enabling retrofit hotspot screening and suitable surrogate model selection before detailed optimization.

The study aims to identify the associated environmental impacts of the soy biodiesel production process. The process is known as transesterification, where the soybean oil reacts with methanol in the presence of another catalyst such as sodium hydroxide. Glycerine is produced as a by-product of this system, and impurities like fatty acids are removed from biodiesel using hydrochloric acid. The upstream stages of this production process, including crude soybean degumming, natural gas combustion, electricity consumption, methanol production, transportation, and other relevant activities, are analysed to calculate environmental impacts. Energy-intensive crude soybean degumming at the plant has been identified as the primary source of carbon footprint for biodiesel production. The findings of this study highlight the importance of conducting a process-based supply chain life cycle assessment (LCA) to assess fuel sustainability. Thus, the soy biodiesel is an alternate renewable fuel; its environmental benefits can be emphasized by improving agricultural efficiency, shifting towards renewable electricity production, and sourcing bio-based chemicals.

Building retrofit optimization has gained increasing attention as a pathway to improve energy performance and support sustainability. This review examines 162 studies and synthesizes simulation-based (SBMOO) and metamodel-based (MBMOO) multi-objective optimization techniques for building retrofit across climatic conditions. The review also analyzes passive, active, and combined retrofit strategies and evaluates how climatic context influences their suitability and performance. Passive strategies typically involve envelope- or material-related upgrades, whereas active strategies focus on building systems. Energy efficiency, comfort, cost-effectiveness, and environmental impact are identified as the major performance metrics for retrofit evaluation. Sustainability metric such as life cycle assessment (LCA) has yet to be used adequately to evaluate retrofit measures, while social objectives are also less explored. SBMOO provides robust optimization but can be computationally intensive, whereas MBMOO improves computational efficiency through surrogate modeling but depends strongly on dataset quality, sampling strategy, and surrogate model selection. In contrast to earlier reviews that usually emphasize either optimization techniques or retrofit measures independently, this study integrates optimization pathway comparison with climate-based analysis of retrofit strategies. The review also finds that most studies are highly case-specific, limiting transferability across climates, building types, and retrofit contexts. Therefore, this work proposes a synthesized framework to support structured selection of baseline modeling and optimization pathways for future retrofit studies. Overall, the review identifies current methodological trends, key research gaps, and future directions for more consistent and climate responsive retrofit decision-making.

This study calculates the carbon footprint of chemical coagulants and operational energy for residential and industrial (whey digestion) wastewater treatment using ReCiPe 2016 methodology within a clearly defined system boundary from cradle to gate. Data from water treatment facilities have been analyzed to quantify environmental impacts and identify sensitive design parameters. The estimated emission of treating 1 m³ of wastewater from whey digestion (7.1195 kg CO₂ eq) is over 50 times higher than that of a residential one (0.1349 kg CO₂ eq). Life cycle impact assessment (LCIA) reveals that iron (III) chloride (40% in H₂O) and operational electricity consumption have higher impact categories compared to other design components. The uncertainty analysis indicates that electricity consumption (r = 0.4) is the dominant contributor to emissions, with a mean value of 4.22 kg CO₂-eq per m³ of wastewater treated. In contrast, iron (III) chloride emerges as the most sensitive parameter (r = 0.88) with small variations in dosing causing a disproportionately large impact on overall emissions. Therefore, the optimized use of an iron-based coagulant, the adoption of membrane electrolysis, and the integration of renewable electricity into the process supply chains have been identified as effective strategies for reducing emissions.