A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill

New paper published on Journal of Cleaner production

Paper Title: A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill

Authors: Chong-chong Qi, Andy Fourie, Qiu-song Chen, Qin-li Zhang

Abstract: The recycling of waste tailings as cemented paste backfill (CPB) has attracted worldwide attention because of the increasing environmental awareness during mineral resources excavation. However, lots of mechanical tests are required to understand the strength development of CPB and its prediction under the combined effect of influencing variables is almost an unexplored field. This study proposes a strength prediction model integrating boosted regression trees (BRT) and particle swarm optimization (PSO), where the BRT algorithm was used for modelling the non-linear relationship between inputs and outputs and PSO was used for the BRT hyper-parameters tuning. An extensive mechanical experiment was performed to provide the dataset for the PSO-BRT model. This dataset contained unconfined compressive strength (UCS) results of 585 CPB specimens produced with a different combination of influencing variables, including the physical and chemical characteristics of tailings, the cement-tailings ratio, the solids content, and the curing time. 10-fold cross validation was used as the validation method, and performance measures were chosen as the mean squared error and the correlation coefficient. The results show that PSO was efficient in the hyper-parameters tuning of the BRT. The optimum BRT model was very accurate at predicting CPB strength. The relative importance of influencing variables was investigated, in which the cement-tailings ratio was found to be the most significant variable for CPB strength. This research indicates that more efficient reuse of waste tailings as CPB can be achieved by reducing the required number of mechanical experiments during engineering applications.

Keywords: Waste tailingsCemented paste backfillRecyclingStrength predictionBoosted regression trees; Particle swarm optimization

Comments from reviews:

Reviewer 1This study investigates the predictability of the unconfined compressive strength (UCS) of cemented paste backfill (CPB) using a new technique named as PSO-BRT method. Four different parameters (tailings type, the cement-tailings ratio, the solids content and the curing time) were selected for the extensive testing program and proposed model. This study has novelty and contributes to mining industry or operators to reliably estimate the UCS of CPB samples. The topic of the manuscript is suitable for the Journal of Cleaner Production. The reviewer suggests some minor revisions to improve paper quality before it can be accepted for publication.

Reviewer 2paper needs minor revision before publication.

Reviewer 3This paper discuss the prediction model of compressive strength of cemented paste backfill (CPB) containing waste tailings by integrating boosted regression trees (BRT)and particle swarm optimization (PSO), the result is useful for simply recycling of waste tailing. The paper proposed a theoretical method for prediction of unconfined compressive strength of cemented paste backfill (CPB) under combined influencing variables, and good relation between predicted and measured results were obtained.

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