Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

Zhang, Guangnan; Ali, Zainab Hasan; Aldlemy, Mohammed Suleman; Mussa, Mohamed H.; Salih, Sinan Q.; Hameed, Mohammed Majeed; Al-Khafaji, Zainab S.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s00366-020-01137-1

“…Among several machine learning models that have been established for shear strength prediction, the SVM model was predominately adopted [57][58][59][60][61]. Hence, the current proposed model (i.e., RF) was validated against the SVM model.…”

Section: Models' Prediction Results and Analysismentioning

confidence: 99%

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

Mohammed

¹

,

Ismail

²

2021

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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).

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“…However, the SVR approaches sometimes provide an acceptable prediction of TDG. For the selection of the most accurate models, the testing set is considered the most efficient step since the models in the training set relying on given input parameters and their responses target, while in the testing set, the actual performance of each model is easily recognized due to the fact that only input variable is introduced to the predictive model [63]. Moreover, in the testing phase, better evaluation of the accuracy of the model as well as generalization capabilities can be efficiently revealed.…”

Section: Resultsmentioning

confidence: 99%

Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach

AlOmar

¹

,

Hameed

²

,

Al‐Ansari

³

et al. 2020

Advances in Civil Engineering

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Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it stands behind the reasons for increasing the mortality rates of fish and aquatic organisms. The accurate and more reliable prediction of TDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths. Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have been applied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For the USGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and the rest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, the hourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from 12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on different parameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy of adopted models, for instance, not limited, correlation of determination ( R 2 ), mean absolute relative error (MAE), and uncertainty at 95% ( U 95 ). The obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG in comparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R 2 of 0.986 (0.986), MAE of 0.316 (0.441), and U 95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R 2 of 0.991 (0.991), MAE of 0.338 (0.396), and U 95 of 0.832 (0.837). In addition, ELM’s training process computational time is stated to be much shorter than that of SVM. The results also showed that the temperature parameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model (ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute to the basic knowledge of environmental considerations.

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“…Some applications of geotechnical and civil construction engineering where AI models can be used include (a) the estimation of soil shear strength related to its ability to bear high load and external pressure due to floods and other natural calamities, (b) the prediction of concrete compressive strength, (c) the prediction of concrete-beam shear strength [ 25 , 26 , 27 ], (d) the prediction of the shear strength of peaks [ 28 ], walls [ 29 ], rocks [ 30 ], etc., and (e) the accurate estimation of pre-project bid cost and duration with minimal risk of cost and duration overrun.…”

Section: Artificial Intelligence and Its Application In Shear Strementioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

¹

,

Ahmed

²

,

Naseem

³

et al. 2021

Sensors

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Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

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Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

Cited by 61 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

Data-Driven Model for the Prediction of Total Dissolved Gas: Robust Artificial Intelligence Approach

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

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