Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

Solhmirzaei, Roya; Salehi, Hadi; Kodur, Venkatesh; Naser, M.Z.

doi:10.1016/j.engstruct.2020.111221

Cited by 129 publications

(32 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI models have profound application in structural engineering owing to the ability to provide remarkable solutions [19][20][21]. AI models can provide solutions to problems associated with high stochasticity, nonlinearity, and nonstationarity.…”

Section: Introductionmentioning

confidence: 99%

“…is is considered an inverse problem and requires that a state should be determined from the observed system behaviour [24]. e problems are first analyzed before finding the solution that will aid in achieving the desired system behavior, while those that will not improve performance are filtered out [21]. AI models can be used to map the behavior of a given system to a space of system attributes that can guarantee the expected behavior.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Mohammed

Ismail

2021

Complexity

View full text Add to dashboard Cite

The shear and bending are the actions that are experienced in the beam owing to the fact that the beam is a flexural member due to the load in the transverse direction to their longitudinal axis. The shear strength (Vs) computation of reinforced concrete (RC) beams has been a major topic in the field of structural engineering. There have been several methodologies introduced for the Vs prediction; however, the modeling accuracy is relatively low owing to the complex characteristic of the resistance mechanism involving dowel effect of longitudinal reinforcement, concrete in the compression zone, contribution of the stirrups if existed, and the aggregate interlock. Hence, the current research proposed a new soft computing model called random forest (RF) to predict Vs. Experimental datasets were collected from the open-source literature including the related geometric properties and concrete characteristics of beam specimens. Nine input combinations were constructed based on the statistical correlation to be supplied for the proposed predictive model. The prediction accuracy of the RF model was validated against the Support Vector Machine (SVM), and several other empirical formulations have been adopted in the literature. The proposed RF model revealed better prediction accuracy in addition the model structure emphasis in the incorporation of seven predictors by excluding (beam flange thickness and coefficient). In the quantitative term, the minimal root mean square error value was attained (RMSE = 89.68 kN).

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Mohammed

Ismail

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Other than the above noted traditional methods, machine learning (ML) continues to present itself as novel and effective approach to tackle data-oriented problems in the civil engineering domain (Naser, 2018;Naser, 2019a;Gandomi et al, 2011;Solhmirzaei et al, 2020;Taffese & Sistonen, 2017;Hodges et al, 2019). For example, ML methods have been proven effective when applied to a variety of problems within the domain of bridge design and maintenance including; bridge assessment , seismic analysis of bridges (Mangalathu & Jeon, 2019), maintenance of bridges (Okazaki et al, 2020), and traffic path planning (Zuo et al, 2019) etc.…”

Section: Introductionmentioning

confidence: 99%

Classifying bridges for the risk of fire hazard via competitive machine learning

Kodur

Naser

2021

ABEN

View full text Add to dashboard Cite

This study presents a machine learning (ML) approach to identify vulnerability of bridges to fire hazard. For developing this ML approach, data on a series of bridge fires was first collected and then analyzed through three algorithms; Random forest (RF), Support vector machine (SVM) and Generalize additive model (GAM), competing to yield the highest accuracy. As part of this analysis, 80 steel bridges and 38 concrete bridges were assessed. The outcome of this analysis shows that the ML based proposed approach can be effectively applied to arrive at the risk based classification of bridges from a fire hazard point of view. In addition, the developed ML algorithms are also capable of identifying the most critical features that govern bridges vulnerability to fire hazard. In parallel, this study showcases the potential of integrating ML into structural engineering applications as a supporting tool for analysis (i.e. in lieu of experimental tests, advanced simulations, and analytical approaches). This work emphasizes the need to compile data on bridge fires from around the world into a centralized and open source database to accelerate the integration of ML in to fire hazard evaluation.

show abstract

“…Machine learning uses samples with input and output for training models that can be used to predict the output of new inputs. At present, machine learning has been used to predict the shear capacity of normal concrete beams or ultra-high strength concrete beams without stirrups (Solhmirzaei et al 2020;Zhang et al 2020). However, few studies were found for applying machine learning approach to predict the shear capacity of ultra-high strength concrete beams with stirrups.…”

Section: Introductionmentioning

confidence: 99%

Modeling shear strength of medium- to ultra-high-strength concrete beams with stirrups using SVR and genetic algorithm

Jiang

Liang

2021

Soft Comput

View full text Add to dashboard Cite

This paper presents a data-driven machine learning approach of support vector regression (SVR) with genetic algorithm (GA) optimization approach called SVR-GA for predicting the shear strength capacity of medium-to ultra-high strength concrete beams with longitudinal reinforcement and vertical stirrups. 148 experimental samples collected with different geometric, material and physical factors from literature were utilized for SVR-GA with 5-fold cross validation. Shear influence factors such as the stirrup spacing, the beam width, the shear span-to-depth ratio, the effective depth of the beam, the concrete compressive and tensile strength, the longitudinal reinforcement ratio, the product of stirrup ratio and stirrup yield strength were served as input variables. The simulation results show that the predicted shear strength of SVR-GA model can achieve high accuracy based on testing set with a coefficient of determination (R 2 ) of 0.9642, root mean squared error (RMSE) of 1.4685 and mean absolute error (MAE) of 1.0216 superior to that for traditional SVR model with 0.9379, 2.0375 and 1.4917. The sensitivity analysis reveals that the most important variables affecting the prediction of the shear strength are shear span-to-depth ratio, concrete compressive strength, reinforcement ratio and the product of stirrup ratio and stirrup yield strength. Three-dimensional input/output maps can vividly reflect the nonlinear variation of the shear strength with the two coupling variables. All in all, the proposed SVR-GA model presents an effective and accurate artificial intelligence technology for modeling the shear strength of ultra-high strength concrete beams with stirrups.

show abstract

Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

Cited by 129 publications

References 60 publications

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength

Classifying bridges for the risk of fire hazard via competitive machine learning

Modeling shear strength of medium- to ultra-high-strength concrete beams with stirrups using SVR and genetic algorithm

Contact Info

Product

Resources

About