Nowadays, lightweight aggregate concrete is becoming more popular due to its versatile properties. It mainly helps to reduce the dead loads of the structure, which ultimately reduces design load requirements. The main challenge associated with lightweight aggregate concrete is finding an optimized mix per requirements. However, the conventional material design of this composite is quite costly, time-consuming, and iterative. This research proposes a simplified methodology for the mix designing of structural and non-structural lightweight aggregate concrete by incorporating machine learning. For this purpose, five distinct machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), decision tree (DT), Gaussian process of regression (GPR), and extreme gradient boosting tree (XGBoost) algorithms, were investigated. For the training, testing, and validation process, a total of 420 data points were collected from 43 published journal articles. The performance of models was evaluated based on statistical performance indicators. Overall, 11 input parameters, including ingredients of the concrete mix and aggregate properties were entertained; the only output parameter was the compressive strength of lightweight concrete. The results revealed that the GPR model outperformed the remaining four machine learning models by attaining an R² value of 0.99, RMSE of 1.34, MSE of 1.79, and MAE of 0.69. In a nutshell, these simplified modern techniques can be employed to make the design of lightweight aggregate concrete easy without extensive experimentation.

In nonstructural infill panels, common materials like expanded polystyrene panels face fire susceptibility, autoclaved aerated concrete (AAC) incurs high production costs, and traditional bricks come with a significant carbon footprint and weight. So, there is a requirement for infill panels that are not just resilient and lightweight but sustainable as well. This study seeks to address these issues by introducing sustainable and lightweight expanded clay aggregate (ECA) in concrete. Firstly, eight ECA mix designs were prepared by integrating fly ash and kerosene with clay, and ECA with a bulk density of 0.59 g/cm³ and compressive strength of up to 1.73 MPa were prepared. The lightest ECA mix was then chosen to explore their use in lightweight aggregate concrete (LWAC) along with fly ash as a secondary cementitious material. The resulting LWAC had a minimum density of 1,050 kg/m³ and a compressive strength of 6.8 MPa, fulfilling the standard requirements of a minimum of 3.5 MPa for nonstructural concrete.

Infill materials play a pivotal role in determining buildings’ life cycle costing (LCC) and environmental impacts. International standards prescribe LCC and life cycle assessments (LCA) to assess materials’ economic and environmental sustainability. The existing methods of LCC and LCA are tedious and time-consuming, reducing their practical application. This study sought to integrate LCC and LCA with building information modeling (BIM) to develop a swift and efficient approach for evaluating the life cycle performance of infill materials. The BIM model for a case study was prepared using Autodesk Revit®, and the study included four infill materials (lightweight aggregate concrete block (LECA), autoclaved cellular concrete (AAC), concrete masonry and bricks). LCC was conducted using Revit® and Autodesk Insight 360® to estimate costs incurred across different project phases. LCA was conducted using “One Click LCA®,” a BIM-based platform featuring a comprehensive material inventory. Carbon emissions, acidification, and eutrophication were chosen as environmental impact factors for LCA. LECA was the preferred choice due to its lower cost and environmental impact. Its lifetime cost of $440,618 was 5.4% lower than bricks’, with 2.8% lower CO₂ emissions than AAC’s, which were second-place options, respectively. LECA had 6.4 and 27% lower costs than concrete blocks, and AAC’s carbon emissions were 32 and 58% lower than concrete blocks and bricks, respectively. BIM has been employed for life cycle analysis in existing literature, but its efficacy in evaluating the lifetime costs and environmental impacts of infill materials remains unexplored. The current study presents a BIM-based approach for conducting LCC and LCA of infill materials, facilitating informed decision-making during the planning phase and promoting sustainable construction practices.