The need for more accurate and robust machine learning models for predicting compressive strength of waste aggregate concrete blocks

The gap

Evidence profile

Sourced from the recommendations and stated research gap and future-work section of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 2 journals. Those papers have been cited 16 times in total.

Supporting evidence — 3 representative gaps

• Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods (2024) · Buildings · cited 16× · doi

  the in implementation of intelligent models in order to achieve an economic effect by improving the process of monitoring the physical and mechanical properties of the concrete in question under the influence of aggressive environmental factors. the construction for participants industry on The main new results of the work are the formation of a dataset during laboratory tests with its subsequent in-depth analysis, as well as increasing the accuracy of the proposed intelligent models using modern approaches. The purpose of this work is to improve the process of managing the life cycle of vibro- centrifuged variatropic concrete through machine learning methods, namely, predicting compressive strength using the extended linear regression method—ridge regression— and algorithms based on decision tree and XGBoost tree structures. The research plan is as follows: (1) Application of existing experience theoretical analysis and practical in implementation of machine learning methods in the life cycle management of vibro- centrifuged variatropic concrete; Justification of the need to expand the stack of technologies to determine the physical and mechanical properties of vibro-centrifuged variatropic concrete by creating regression models based on machine learning methods; (2) (3) Testing of samples made of vibro-centrifuged variatropic concrete under laboratory conditions, with the subsequent formation of a dataset for the training, optimization and testing of regression models; Buildings 2024, 14, 1198 4 of 23 (4) Analysis of the data obtained, identifying the main statistical characteristics and determining dependencies; (5) Creating an expanded dataset by adding new features at the feature engineering stage; (6) Description and implementation of the ridge regression method on original dataset and feature-engineered dataset; (7) Description and implementation of the decision tree and XGBoost method on original dataset and feature-engineered dataset; (8) Description and implementation of the XGBoost method on original dataset and feature-engineered dataset; (9) Comparative analysis of the results of all models based on the values of the main metrics to assess the quality of the forecast when solving a regression problem; (10) Determination of prospects and features of implementation of developed forecasting methods into practice; (11) Determining the possibility of “learning transfer” by adapting the results obtained to other types of concrete.

  generalrecommendations
  Keywords: dataset implementation concrete regression models vibro centrifuged variatropic learning feature main machine based tree xgboost
• Predicting the strength of waste aggregate concrete blocks using novel hybrid machine learning models and graphical user interface deployment (2026) · Scientific Reports · doi

  The need for more accurate and robust machine learning models for predicting compressive strength of waste aggregate concrete blocks. The lack of novel hybrid machine learning models that can provide excellent predictive accuracy. The need for a user-friendly interface to make predictive models accessible for practical engineering applications.

  generalstated research gapevidence 5/5
  Keywords: need accurate robust machine learning models predicting compressive
• A hybrid machine learning approach for predicting the flexural strength of concrete reinforced with waste aluminium fibres (2026) · Scientific Reports · doi

  Future research should focus on exploring the application of machine learning models for predicting other mechanical properties of concrete. The study of other types of fibre reinforcement and their effects on concrete properties is necessary. Further research is needed to develop more accurate and robust machine learning models for predicting flexural strength.

  generalfuture-work sectionevidence 4/5
  Keywords: future research focus exploring application machine learning models

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