This paper reports an investigation of the mechanical and microscopic properties of partially replaced banana leaf ash (BLA) concrete. In this research, the cement was partially replaced by BLA in two phases: Phase A (0%, 5%, 10%, 15%, 20%, 25% and 30%) and Phase B (0%, 10%, 20% and 30%). The consequence of partially replacing cement with BLA in concrete was investigated by the application of a range of tests, namely X-ray fluorescence (XRF), compressive strength, splitting tensile strength, flexure strength, ultrasonic pulse velocity and scanning electron microscopy (SEM) analysis. The properties were then correlated with the properties of a standard 100% Portland cement concrete of similar strength. The XRF result of the BLA identified a composition with 48.93% SiO₂ and 3.48% Al₂O_3, which indicates that the material potentially possesses pozzolanic properties. The mechanical properties of the partially replaced BLA concrete noted minor strength loss, approximately 5% with 20% partial replacement. The nondestructive testing data showed enhanced performance up to 20% partial replacement, with ultrasonic pulse values above 3500 m/s. The scanning electron microscopy analysis illustrated that the morphology of BLA specimens contained increased microcracks compared with the control. The decrease in strength observed is attributed to the fibrous composition of the BLA. The mechanical, nondestructive testing and microscopic results highlight the potential to utilize BLA as a partial replacement for cement as a pozzolanic material in concrete at up to 20% by weight of cement.

Purpose: In response to rising energy prices and growing environmental concerns, there is a growing demand for environmentally friendly building facilities. This study investigates optimizing energy consumption and improves the level of accuracy when selecting suitable materials and components with minimal impact on the overall energy consumption of buildings.Design/methodology/approach: This study was carried out from the perspective of an educational building's energy simulation, using a validated building energy analysis tool Green Building Studio (GBS). There were eight parameters analyzed considering at least two connected variables without measuring the initial building configuration. After that, Autodesk Revit and Insight 360 were used to make similar scenarios of the best performance selections so that the general results could be compared and the initial hypothesis could be proven.Findings: In this study, the initial building analysis showed that there was an annual energy use of 139 kBtu/sf and the estimated carbon emissions were about 156 tons/yr. After the parametric analysis, the maximum energy saving was about 32.38%, considering the best performance scenario with a reduction of CO₂ emissions of around 28.85%.Originality/value: The outcome of this study will help Bangladeshi architect/designers to make appropriate decisions regarding the selection of suitable building materials and components at the initial stage of any project in terms of the energy consumption aspects. In addition, energy-efficient buildings provide cleaner combustion and better circulation than traditional buildings, that is why they reduce indoor air pollution, maintaining a safe, healthy and sustainable environment for future generations.

This study explores the combined use of volcanic pumice powder (VPP) and recycled nylon fiber (RNF), sourced from discarded fishing nets, in developing sustainable concrete. Ten mixtures were designed using 10–20% VPP as a partial cement replacement alongside 0–2% RNF. Fresh-state behavior, mechanical performance (compressive, tensile, and flexural strength), and non-destructive testing (rebound hammer and ultrasonic pulse velocity) were assessed, while SEM-based microstructural analysis confirmed pore refinement and enhanced C–S–H gel formation. The 10% VPP–1% RNF mixture achieved the highest compressive strength, exceeding the control by about 18%, while 10% VPP–2% RNF delivered the best tensile and flexural results, improving by roughly 35% and 34%, respectively. Environmentally, 20% VPP replacement reduced CO₂ emissions by nearly 20%, highlighting the mixture’s sustainability benefits. Machine learning models were also applied to predict mechanical properties, with the Gradient Boosting Regressor outperforming Random Forest and XGBoost, achieving a test R² of 0.924 and MAE of 6.75 MPa. Feature importance analysis identified curing age, supplementary cementitious materials, and cement content as the most influential parameters. Overall, the VPP–RNF combination enhances concrete’s strength, durability, and environmental performance, while machine learning provides reliable, interpretable tools for optimizing sustainable concrete mix design.