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This study explores the contributions of fly ash, bottom ash and biomass ash from coal and biomass power plants for enhancing circular economy of construction sectors in emerging economies.

This research investigates their applications in construction, emphasizing their role in reducing environmental impact and promoting circular economy principles. Through a qualitative analysis using data from structured interviews with 41 involved stakeholders, the study highlights the economic and environmental benefits of integrating these by-products into business operations.

Currently, the cement and concrete industries can successfully adopt almost 100% fly ash, but logistic optimization is necessary to address the wet fly ash problem. The practical applications of bottom ash pose disposal challenges due to their poor adoption. Biomass ash can be alternatively implemented as a soil amendment and fertilization in the agriculture industry while current growth seems significant with the shift to a clean energy policy.

This research underscores the importance of policy support and collaboration between industry stakeholders to maximize the sustainable potential of these by-products in an emerging economy context.

The sustainability development goals (SDGs) were well-established in developing economies. Nevertheless, the literature review indicates that there is a lack of understanding regarding their backgrounds, influencing factors, challenges and practical applications for the circular economy.

This paper aims to clarify the efficient process of the machine learning algorithms implemented in the ready-mix concrete (RMC) onsite. It proposes innovative machine learning algorithms in terms of preciseness and computation time for the RMC strength prediction.

This paper presents an investigation of five different machine learning algorithms, namely, multilinear regression, support vector regression, k-nearest neighbors, extreme gradient boosting (XGBOOST) and deep neural network (DNN), that can be used to predict the 28- and 56-day compressive strengths of nine mix designs and four mixing conditions. Two algorithms were designated for fitting the actual and predicted 28- and 56-day compressive strength data. Moreover, the 28-day compressive strength data were implemented to predict 56-day compressive strength.

The efficacy of the compressive strength data was predicted by DNN and XGBOOST algorithms. The computation time of the XGBOOST algorithm was apparently faster than the DNN, offering it to be the most suitable strength prediction tool for RMC.

Since none has been practically adopted the machine learning for strength prediction for RMC, the scope of this work focuses on the commercially available algorithms. The adoption of the modified methods to fit with the RMC data should be determined thereafter.

The selected algorithms offer efficient prediction for promoting sustainability to the RMC industries. The standard adopting such algorithms can be established, excluding the traditional labor testing. The manufacturers can implement research to introduce machine learning in the quality controcl process of their plants.

Regarding literature review, machine learning has been assessed regarding the laboratory concrete mix design and concrete performance. A study conducted based on the on-site production and prolonged mixing parameters is lacking.