Ensemble learning models to predict the compressive strength of geopolymer concrete: a comparative study for geopolymer composition design

Tian, Qiong; Su, Zhanlin; Fiorentini, Nicholas; Zhou, Ji; Luo, Hao; Lu, Yijun; Xu, Xingquan; Chen, Chupeng; Huang, Jiandong

doi:10.1007/s41939-023-00303-4

Cited by 3 publications

(3 citation statements)

References 66 publications

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“…Table 10 summarizes the descriptive statistics of the abovementioned parameters. For more data analysis, the Pearson correlation coefficients between 17 input parameters and CSFAGC are determined by using Equation (18) [134][135][136][137], with the obtained Pearson correlation coefficients depicted in Figure 11. The figure demonstrates the level at which CSFAGC establishes correlations with the inputs.…”

Section: Data Descriptionmentioning

confidence: 99%

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

Shi,

Chen,

Wang

et al. 2024

Gels

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As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources to prepare the cementitious component of the product. The challenging issue with employing geopolymer concrete in the building business is the absence of a standard mix design. According to the chemical composition of its components, this work proposes a thorough system or framework for estimating the compressive strength of fly ash-based geopolymer concrete (FAGC). It could be possible to construct a system for predicting the compressive strength of FAGC using soft computing methods, thereby avoiding the requirement for time-consuming and expensive experimental tests. A complete database of 162 compressive strength datasets was gathered from the research papers that were published between the years 2000 and 2020 and prepared to develop proposed models. To address the relationships between inputs and output variables, long short-term memory networks were deployed. Notably, the proposed model was examined using several soft computing methods. The modeling process incorporated 17 variables that affect the CSFAG, such as percentage of SiO2 (SiO2), percentage of Na2O (Na2O), percentage of CaO (CaO), percentage of Al2O3 (Al2O3), percentage of Fe2O3 (Fe2O3), fly ash (FA), coarse aggregate (CAgg), fine aggregate (FAgg), Sodium Hydroxide solution (SH), Sodium Silicate solution (SS), extra water (EW), superplasticizer (SP), SH concentration, percentage of SiO2 in SS, percentage of Na2O in SS, curing time, curing temperature that the proposed model was examined to several soft computing methods such as multi-layer perception neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFNN), support vector regression (SVR), decision tree (DT), random forest (RF), and LSTM. Three main innovations of this study are using the LSTM model for predicting FAGC, optimizing the LSTM model by a new evolutionary algorithm called the marine predators algorithm (MPA), and considering the six new inputs in the modeling process, such as aggregate to total mass ratio, fine aggregate to total aggregate mass ratio, FASiO2:Al2O3 molar ratio, FA SiO2:Fe2O3 molar ratio, AA Na2O:SiO2 molar ratio, and the sum of SiO2, Al2O3, and Fe2O3 percent in FA. The performance capacity of LSTM-MPA was evaluated with other artificial intelligence models. The results indicate that the R2 and RMSE values for the proposed LSTM-MPA model were as follows: MLPNN (R2 = 0.896, RMSE = 3.745), BRNN (R2 = 0.931, RMSE = 2.785), GFFNN (R2 = 0.926, RMSE = 2.926), SVR-L (R2 = 0.921, RMSE = 3.017), SVR-P (R2 = 0.920, RMSE = 3.291), SVR-S (R2 = 0.934, RMSE = 2.823), SVR-RBF (R2 = 0.916, RMSE = 3.114), DT (R2 = 0.934, RMSE = 2.711), RF (R2 = 0.938, RMSE = 2.892), LSTM (R2 = 0.9725, RMSE = 1.7816), LSTM-MPA (R2 = 0.9940, RMSE = 0.8332), and LSTM-PSO (R2 = 0.9804, RMSE = 1.5221). Therefore, the proposed LSTM-MPA model can be employed as a reliable and accurate model for predicting CSFAG. Noteworthy, the results demonstrated the significance and influence of fly ash and sodium silicate solution chemical compositions on the compressive strength of FAGC. These variables could adequately present variations in the best mix designs discovered in earlier investigations. The suggested approach may also save time and money by accurately estimating the compressive strength of FAGC with low calcium content.

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Section: Data Descriptionmentioning

confidence: 99%

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

Shi,

Chen,

Wang

et al. 2024

Gels

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“…The interfacial transition zone (ITZ), which is known to be a vulnerable regio concrete, is improved as a consequence of the thorough filling of gaps and voids by t nanoparticles [18][19][20], which ultimately results in a reduction in permeability. It has shown via research that NS is an extremely efficient component that speeds up the pro of concrete hydration [21] and encourages the production of calcium-silicate-hydrat S-H) gel, which is an essential factor in determining the strength of the material [22 NS interacts with Ca(OH)2, which results in a denser final product [25,26]. This cause percentage of portlandite-Ca(OH)2 to decrease in cementitious materials like cementi materials.…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown via research that NS is an extremely efficient component that speeds up the process of concrete hydration [21] and encourages the production of calciumsilicate-hydrate (C-S-H) gel, which is an essential factor in determining the strength of the material [22][23][24]. NS interacts with Ca(OH) 2 , which results in a denser final product [25,26]. This causes the percentage of portlandite-Ca(OH) 2 to decrease in cementitious materials like cementitious materials.…”

Section: Introductionmentioning

confidence: 99%

Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Wang,

Zhang,

et al. 2024

Buildings

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Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a concrete sculpture to address the issue of brittle fracture, and the primary objective of this study was to explore the potential of hybridizing the Grey Wolf Optimizer (GWO) with four robust and intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, and CatBoost, to anticipate the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) for sculptural elements. The optimization of hyperparameters for these techniques was performed using the GWO metaheuristic algorithm, enhancing accuracy through the creation of four hybrid ensemble learning models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, and GWO-CatBoost. A comparative analysis was conducted between the results obtained from these hybrid models and their conventional counterparts. The evaluation of these models is based on five key indices: R2, RMSE, VAF, MAE, and bias, addressing an objective assessment of the predictive models’ performance and capabilities. The outcomes reveal that GWO-XGBoost, exhibiting R2 values of (0.971 and 0.978) for the train and test stages, respectively, emerges as the best predictive model for estimating the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) compared to other models. Consequently, the proposed GWO-XGBoost algorithm proves to be an efficient tool for anticipating CSFrRNSC.

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Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete

Tian,

Lu,

Zhou

et al. 2024

Reviews on Advanced Materials Science

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As a potential replacement for traditional concrete, which has cracking and poor durability issues, self-healing concrete (SHC) has been the research subject. However, conducting lab trials can be expensive and time-consuming. Therefore, machine learning (ML)-based predictions can aid improved formulations of self-healing concrete. The aim of this work is to develop ML models that could analyze and forecast the rate of healing of the cracked area (CrA) of bacteria- and fiber-containing SHC. These models were constructed using gene expression programming (GEP) and multi-expression programming (MEP) tools. The discrepancy between expected and desired results, statistical tests, Taylor’s diagram, and R 2 values were additional metrics used to assess the constructed models. A SHapley Additive exPlanations (SHAP) approach was used to evaluate which input attributes were highly relevant. With R 2 = 0.93, MAE = 0.047, MAPE = 12.60%, and RMSE = 0.062, the GEP produced somewhat worse predictions than the MEP (R 2 = 0.93, MAE = 0.033, MAPE = 9.60%, and RMSE = 0.044). Bacteria had an indirect (negative) relationship with the CrA of SHC, while fiber had a direct (positive) association, according to the SHAP study. The SHAP study might help researchers and companies figure out how much of each raw material is needed for SHCs. Therefore, MEP and GEP models can be used to generate and test SHC compositions based on bacteria and polymeric fibers.

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Ensemble learning models to predict the compressive strength of geopolymer concrete: a comparative study for geopolymer composition design

Cited by 3 publications

References 66 publications

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete

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