A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Jin, Depeng; Asteris, Panagiotis G.; Nguyen, Hoang; Bui, Xuan‐Nam; Moayedi, Hossein

doi:10.1007/s00366-020-01003-0

Cited by 197 publications

(40 citation statements)

References 86 publications

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“…Unlike the AI methods, almost all of the traditional statistical methods rely on the prior knowledge of the nature of the relationship between dependent and independent variables [240][241][242][243]. Given their capability in capturing subtle knowledge without a need to assume prior form of the relationship, AI has become a powerful component in integrated systems or an alternative approach to conventional techniques, which is typically applied to resolve complicated practical problems in various fields such as materials science [244][245][246][247][248][249][250] and engineering [251][252][253][254][255][256][257][258][259][260][261][262][263][264][265]. Due to the inadequacy of physics-based models developed using the first principles, AI has recently attracted significant attention in architected materials and structures [266][267][268][269][270][271][272].…”

Section: Ai and Its Applications In Architected Materials And Structuresmentioning

confidence: 99%

Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

Jiao

Alavi

2020

International Materials Reviews

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Mechanical metamaterials have opened an exciting venue for control and manipulation of architected structures in recent years. Research in the area of mechanical metamaterials has covered many of their fabrication, mechanism characterisation and application aspects. More recently, however, a paradigm shift has emerged to an exciting research direction towards designing, optimising and characterising mechanical metamaterials using artificial intelligence (AI) techniques. This new line of research aims at addressing the difficulties in mechanical metamaterials (i.e. design, analysis, fabrication and industrial application). This review article discusses the advent and development of mechanical metamaterials, and the future trends of applying AI to obtain smart mechanical metamaterials with programmable mechanical response. We explain why architected materials and structures have prominent advantages, what are the main challenges in the mechanical metamaterial research domain, and how to surpass the limit of mechanical metamaterials via the AI techniques. We finally envision the potential research avenues and emerging trends for using the AI-enabled mechanical metamaterials for future innovations.

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Section: Ai and Its Applications In Architected Materials And Structuresmentioning

confidence: 99%

Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

Jiao

Alavi

2020

International Materials Reviews

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“…Some researchers have employed indirect testing methods, i.e., multiple linear regression (MLR) [17,22,23], artificial neural network (ANN) [23,24], adaptive neurofuzzy inference system (ANFIS) [25], and other machine learning algorithms to estimate the accuracy and reliability of rock data [26][27][28], rather than using direct tests recommended by international standards, which are considered time-consuming, expensive, and unreliable [29,30]. While these frameworks are appropriate, fast, and favorable techniques to tackle difficult problems, in most cases, they are simply capable of understanding the complex interactions among variables to estimate an objective and do not provide any intuition about the interrelationships among predictors and a return value [31].…”

Section: Introductionmentioning

confidence: 99%

Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield

Shahani

Kamran

Zheng

et al. 2021

Advances in Civil Engineering

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The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm3; moisture in %; dry density (ρd) in g/cm3; and Brazilian tensile strength (BTS) in MPa. Then, 106-point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρw are positively correlated, and the moisture and ρd are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan.

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“…Thus, attention must be paid to the indirect evaluation of E through the use of rock index tests. Many researchers have established prediction models to overcome these shortcomings by employing soft computing methods such as artificial neural network (ANN), multiple regression analysis (MRA) and other novel machine learning approaches (Lindquist et al, 1994;Singh and Dubey, 2000;Tiryaki, 2008;OzcelikBayram et al, 2013;Abdi et al, 2018;Teymen and Mengüç, 2020;Cao et al, 2021;Yang et al, 2020;Duan et al, 2020). Waqas et al used linear and nonlinear regression, regularization and ANFIS (using neuro-fuzzy inference system) to predict the E d of sedimentary rocks (Waqas and Ahmed, 2020) (Elkatatny et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

et al. 2021

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Young’s modulus (E) is essential for predicting the behavior of materials under stress and plays an important role in the stability of surface and subsurface structures. E has a wide range of applications in mining, geology, civil engineering, etc.; for example, coal and metal mines, tunnels, foundations, slopes, bridges, buildings, drilling, etc. This study developed a novel machine learning regression model, namely an extreme gradient boosting (XGBoost) to predict the influences of four inputs such as uniaxial compressive strength in MPa; density in g/cm3; p-wave velocity (Vp) in m/s; and s-wave velocity in m/s on two outputs, namely static Young’s modulus (Es) in GPa; and dynamic Young’s modulus (Ed) in GPa. Using a series of basic statistical analysis tools, the accompanying strengths of each input and each output were systematically examined to classify the most prevailing and significant input parameters. Then, two other models i.e., multiple linear regression (MLR) and artificial neural network (ANN) were employed to predict Es and Ed. Next, multiple linear regression and ANN were compared with XGBoost. The original dataset was allocated as 70% for the training stage and 30% for the testing stage for each model. To improve the performance of the developed models, an iterative 10-fold cross-validation method was used. Therefore, based on the results XGBoost model has revealed the best performance with high accuracy (Es: correlation coefficient (R2) = 0.998; Ed: R2 = 0.999 in the training stage; Es: R2 = 0.997; Ed: R2 = 0.999 in the testing stage), root mean square error (RMSE) (Es: RMSE = 0.0652; Ed: RMSE = 0.0062 in the training stage; Es: RMSE = 0.071; Ed: RMSE = 0.027 in the testing stage), RMSE-standard deviation ratio (RSR) index value (Es: RSR = 0.00238; Ed: RSR = 0.00023 in the training stage; Es: RSR = 0.00304; Ed: RSR = 0.001 in the testing stage) and variance accounts for (VAF) (Es: VAF = 99.71; Ed: VAF = 99.99 in the training stage; Es: VAF = 99.83; Ed: VAF = 99.94 in the testing stage) compared to the other developed models in this study. Using a novel machine learning approach, this study was able to deliver substitute elucidations for predicting Es and Ed parameters with suitable accuracy and runtime.

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A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Cited by 197 publications

References 86 publications

Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield

Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

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