Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

Oyebisi, Solomon; Alomayri, Thamer

doi:10.1016/j.conbuildmat.2023.132606

Cited by 27 publications

(2 citation statements)

References 125 publications

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“…DNN is a feedforward network that uses multiple hidden layers composed of neurons to analyze complex relationships between inputs and target features [104][105][106][107]. DRF combines multiple weak decision trees to produce a strong ensemble forest [108,109].…”

Section: Wqi Model Id Equation Location Sourcementioning

confidence: 99%

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Bygate,

Ahmed

2024

Remote Sensing

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Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

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Section: Wqi Model Id Equation Location Sourcementioning

confidence: 99%

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Bygate,

Ahmed

2024

Remote Sensing

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“…[13,18]. Geopolymer concrete (GPC) has drawn extensive interest from specialists attributable to its promising potential when contrasted with common Portland concrete (OPC) [23][24][25]. There is presently a change in research center from science to design, with an emphasis on investigating the business creation of GPC [2,12,15].…”

Section: Figure 1 Geopolymerization Process [12]mentioning

confidence: 99%

GGBFS and Red-Mud based Alkali-Activated Concrete Beams: Flexural, Shear and Pull-Out Test Behavior

Al-Jabali,

El-Latief,

Ezz

et al. 2024

Civ Eng J

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Geopolymers and antacid-enacted fasteners have accumulated critical interest as promising development and fixing materials because of their exceptional properties. Also, they bring about less contamination contrasted with regular concrete cements. Geopolymers address a clever class of suggested restricting materials blended through the basic enactment of bountiful aluminosilicate materials. The usage of geopolymer materials from side effects offers a critical decrease in carbon impression and yields positive natural effects. Geopolymer is progressively recognized as a plausible substitute for OPC concrete. In this review, sodium-based antacid activators, especially sodium metasilicate (Na2SiO3), were used for different blend extents. The boundaries researched included NaOH arrangements with a grouping of 8 M, alongside a Na2SiO3/NaOH proportion of 1. This paper evaluates the fundamental characteristics of geopolymer cement beams, employing red mud and GGBFS in powdered form as complete replacements for traditional concrete. Six bar specimens are tested under a two-point static loading condition, all cured at room temperature under ambient conditions. Of the six beams, three were exposed to flexural conduct testing with a molarity of 8 M, while the excess three beams were tried for shear conduct. The outcomes of testing geopolymer beams subjected to shear and bending loads indicated that the beams incorporating aluminum slag performed better than those incorporating blast furnace slag. Both types also demonstrated promising results compared to beams incorporating OPC, highlighting their potential environmental benefits compared to cement use. Doi: 10.28991/CEJ-2024-010-05-09 Full Text: PDF

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Predictive modeling of compressive strength of geopolymer concrete before and after high temperature applying machine learning algorithms

Yang,

Li,

Jiang

2024

Structural Concrete

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Geopolymer concrete (GPC) is regarded as a more environmentally friendly construction material compared to conventional cement concrete, and its exceptional environmental capabilities are highly favored by the contemporary construction sector. Studying the mechanical properties of GPC upon exposure to elevated temperatures is a crucial aspect of evaluating structural damage and enhancing fire safety measures. Nevertheless, properly predicting the compressive performance of GPC upon exposure to high temperatures remains a formidable task. This study employs various machine learning techniques, such as single models, integrated models, neural network models, and hybrid models, to predict the compressive strength of GPC from room temperature to 1000°C. The results of each model are summarized, and the significant factors influencing compressive strength are analyzed to evaluate the thermal behavior of GPC. These findings offer recommendations for future in‐depth machine learning applications in the GPC field. The K‐fold cross‐validation shows that the hybrid model genetic algorithm–random forest has the highest prediction accuracy, while the single model performs the worst. Other models also provide favorable prediction results. The feature importance analysis revealed that the compressive strength of GPC is primarily influenced by heating temperature (HT) and hydroxide ion concentration, with fly ash and ground granulated blast furnace slag content being secondary factors. The partial dependence plot‐2D analysis indicates that as HTs increase, the influence of other variables on GPC compressive strength decreases significantly. These findings can inform the design of GPC mixing ratios for high‐temperature exposure. The machine learning technique proposed in this study accurately predicts GPC compressive strength across various temperatures, reducing experimental time and costs while promoting the GPC sector.

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Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

Cited by 27 publications

References 125 publications

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

GGBFS and Red-Mud based Alkali-Activated Concrete Beams: Flexural, Shear and Pull-Out Test Behavior

Predictive modeling of compressive strength of geopolymer concrete before and after high temperature applying machine learning algorithms

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