Concrete is the most highly used construction material globally. This is largely due to its durability, versatility and manufacture from inexpensive and readily available materials. Although concrete has become an essential and ubiquitous construction material for modern society, its use has significant environmental impacts. The full cement and concrete lifecycle, from production to final disposal, accounts for nearly 10% of global energy-related CO2 emissions with the majority of these emissions produced from cement, which is the binding material that holds concrete together. The cement and concrete industry (CCI), which is integral to global infrastructure development, is therefore confronted with a growing need to decarbonize its operations and products, as well as to support the decarbonization of associated end-user sectors. This paper provides a systematic and critical review of more than 800 studies to highlight ways in which the CCI can decarbonize. A socio-technical perspective is used to understand the full range of industrial and economic activities where a decarbonized paradigm for cement and concrete production is relevant. This perspective is further used to assess key technical, economic, social and political factors that will drive a net-zero transition in the CCI over the long term. 

Decarbonizing the cement and concrete industry: A systematic review of socio-technical systems, technological innovations, and policy options

Techno-socio-economic aspects of Portland cement, Geopolymer, and Limestone Calcined Clay Cement (LC3) composite systems: A-State-of-Art-Review

Performances and microstructure of one-part fly ash geopolymer activated by calcium carbide slag and sodium metasilicate powder

Analysis on factors affecting moisture stability of steel slag asphalt concrete using grey correlation method

The strength of carbon nanotubes (CNTs) and cement composites is dependent on multiple variables. In addition, CNTs added to a cement-based matrix can boost its strength. However, the information related to CNTs characteristics is limited and scarce. Their incorporation may substantially enhance the mechanical and durability properties of cementitious mixtures. Despite challenges such as high cost and workability problems. Therefore, proper consumption of these materials must be used to attain desired qualities. The principal plan of this investigation is to create predictive framework by utilizing machine-learning algorithms. Gene expression programming (GEP), and the random forest algorithm (RFA) is employed to estimate the compressive strength of concrete mixed with CNTs. GEP is used as an individual approach, and RFA is used as an ensemble method to depict the most influential model. The outcomes of the two models are assessed by employing external K-fold cross-validation, and a comparison is done. A comprehensive database is established comprising 282 data points for the CS with blended CNTs. The model is then calibrated using six inputs, including curing time (CT), water-to-cement ratio (W/C), fine aggregate (FA), carbon nanotube content (CNTs), cement content (CC), and coarse aggregate (CA). In addition, the predicted results are validated using k-fold cross-validation, and performance metrics, such as mean absolute error (MAE), root squared error (RSE), correlation coefficient (R2), root mean square error (RMSE), and relative root mean square error (RRMSE). The result shows that RF regression with the nth estimator shows robust accuracy by showing minimal errors as analyzed to individual RF and GEP models. Likewise, the nth model depicts higher R2 = 0.96, and validation results demonstrate low errors. Moreover, the GEP model excels in terms of prediction through the empirical equation. In addition, Shapley analysis (SHAP) is performed to check the distribution of parameters to output. The result reveals that curing time, cement, and water to binder have substantial influence of CNT based concrete composite. 

Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: Hyper-tuning and optimization

Environmental life-cycle assessment of concrete produced in the United States

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Environmental life-cycle assessment of concrete produced in the United States