Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction

Sharafati, Ahmad; Haghbin, Masoud; Aldlemy, Mohammed Suleman; Mussa, Mohamed H.; Zand, Ahmed W. Al; Ali, Mumtaz; Bhagat, Suraj Kumar; Al‐Ansari, Nadhir; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.3390/app10113811

Cited by 19 publications

(10 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to improve the predictive performance during the design of RC beams to enable the accurate prediction of the shear strength of different types of RC beams. Several proposals have been made in recent years regarding the use of advanced machine learning (ML) models for shear strength prediction [13,14].…”

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

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%

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

“…The initial stiffness ( K i ) and serviceability stiffness ( K s ) levels of tested specimens are usually measured from the moment vs. mid-span curvature relationships [ 50 , 51 , 52 , 53 ], which are based on moment values of 0.2 M u and 0.6 M u , respectively. The K i and K s values of the tested specimens are presented in Table 1 .…”

Section: Discussion Of Experimental Resultsmentioning

confidence: 99%

Flexural Strength of Internally Stiffened Tubular Steel Beam Filled with Recycled Concrete Materials

et al. 2021

Self Cite

View full text Add to dashboard Cite

The flexural strength of Slender steel tube sections is known to achieve significant improvements upon being filled with concrete material; however, this section is more likely to fail due to buckling under compression stresses. This study investigates the flexural behavior of a Slender steel tube beam that was produced by connecting two pieces of C-sections and was filled with recycled-aggregate concrete materials (CFST beam). The C-section’s lips behaved as internal stiffeners for the CFST beam’s cross-section. A static flexural test was conducted on five large scale specimens, including one specimen that was tested without concrete material (hollow specimen). The ABAQUS software was also employed for the simulation and non-linear analysis of an additional 20 CFST models in order to further investigate the effects of varied parameters that were not tested experimentally. The numerical model was able to adequately verify the flexural behavior and failure mode of the corresponding tested specimen, with an overestimation of the flexural strength capacity of about 3.1%. Generally, the study confirmed the validity of using the tubular C-sections in the CFST beam concept, and their lips (internal stiffeners) led to significant improvements in the flexural strength, stiffness, and energy absorption index. Moreover, a new analytical method was developed to specifically predict the bending (flexural) strength capacity of the internally stiffened CFST beams with steel stiffeners, which was well-aligned with the results derived from the current investigation and with those obtained by others.

show abstract

“…With all the considered methods, the first eight years (2010-2017) are used to construct model. Then, H = [1,6,12]-steps ahead predictions are computed based on the constructed models and all the three-error metrics (e.g., RMSPE, MAPE, and AAPRPE) are computed. While doing so, first, the 96 observations in the dataset (eight years of data), called training data, are used to construct models and to predict H = [1, 6, 12]steps ahead observations.…”

Section: Application Results and Assessmentmentioning

confidence: 99%

“…Moreover, dew point temperature is an important parameter for the prediction of long-term changes in climate [8]. Over the last few decades, artificial intelligence (AI) models have found vast application in the modeling and estimation of various engineering and sciences problems [9]- [12]. However, a remarkable advancement has been observed within the natural process applications such as hydrology [13], [14], climate [15], morphologies [16], [17] and environnement [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Dew Point Time Series Forecasting at the North Dakota

Arikan¹,

Jiechen²,

Sabbah³

et al. 2021

Kbes

View full text Add to dashboard Cite

Hydrological time series forecasting is one of the hot topics in the domain of statistical hydrology. Providing accurate forecasting can contribute to diverse applications for catchment sustainability and management. Dew point temperature (Tdew) is one of the complex hydrological processes that highly essential to be quantified accurately for several catchment activities such as crops, agriculture, and others. In this study, three types of models’ recursive strategy, direct strategy, and DirRec which is the combination of recursive and direct strategies were adopted to obtain h-steps ahead predictions of Tdew. Ten years monthly scale dataset of Tdew at two meteorological stations (Beach and Cavalier) located at the North Dakota, USA, were used for the modeling development. The performance of the considered models was compared with two benchmark models: autoregressive moving average (ARIMA) and exponential smoothing (ETS). Modeling results indicated that, compared with the benchmark models, the proposed methods gave good results for the multi-ahead forecasting. For instance, for Cavalier station, the root mean squared prediction errors obtained from the proposed and benchmark methods when the forecast horizon is 12 are as follows: recursive strategy (RMSPE = 3.731) direct strategy (RMSPE = 3.385), DirRec (RMSPE = 3.141), ARIMA (RMSPE = 12.957), and ETS (RMSPE = 27.479).

show abstract

Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction

Cited by 19 publications

References 76 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

Flexural Strength of Internally Stiffened Tubular Steel Beam Filled with Recycled Concrete Materials

Dew Point Time Series Forecasting at the North Dakota

Contact Info

Product

Resources

About