Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies.Peer ReviewedPostprint (published version

Data-driven optimization tool for the functional, economic, and environmental properties of blended cement concrete using supplementary cementitious materials

The need to produce more sustainable concrete is proving imminent given the rising environmental concerns facing the industry. Blended cement concrete, based on any of the prominent supplementary cementitious materials (SCMs) such as fly ash, ground granulated blast-furnace slag, silica fume, calcined clay and limestone powder, have proven to be the best candidates for sustainable concrete mixes. However, a reliable sustainability measure includes not only the environmental impact, but also the economic and functional ones. Within these five SCMs, their environmental, economic and functional properties are found to be conflicting at times, making a clear judgement on what would be the optimum mix not a straightforward path. A recent framework and tool for concrete sustainability assessment ECO2, sets a reliable methodology for including the functional performance of a concrete mix depending on project-based specifications. Therefore, in this study, a recently published regression model, Pre-bcc was used to predict the functional properties of a wide grid search of potentially suitable blended cement concrete mixes. Hence, an open access novel genetic algorithm tool “Opt-bcc” was developed and used to optimize the sustainability score of these mixes based on a set selection of user-defined project-specific functional criteria. The optimized mixes using the Opt-bcc model for each strength class were compared against the mix design proposed by other optimization models from the literature and were found to be at least 70% cheaper and of 30% less environmental impact.Peer ReviewedPostprint (published version