Shahrizan Baharom

Bio: Shahrizan Baharom is an academic researcher from National University of Malaysia. The author has contributed to research in topics: Beam (structure) & Compressive strength. The author has an hindex of 12, co-authored 64 publications receiving 509 citations.

Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

Abstract: Structural design of a composite beam is influenced by two main factors, strength and ductility. For the design to be effective for a composite beam, say an RC slab and a steel I beam, the shear strength of the composite beam and ductility have to carefully estimate with the help of displacements between the two members. In this investigation the shear strengths of steel-concrete composite beams was analyzed based on the respective variable parameters. The methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) has been adopted for this purpose. The detection of the predominant factors affecting the shear strength steel-concrete composite beam was achieved by use of ANFIS process for variable selection. The results show that concrete compression strength has the highest influence on the shear strength capacity of composite beam.

Strength prediction of rotary brace damper using MLR and MARS

Abstract: This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

Direct shear behavior of concrete filled hollow steel tube shear connector for slim-floor steel beams

Abstract: In this paper, a hollow steel tube (HST) shear connector is proposed for use in a slim-floor system. The HST welded to a perforated steel beam web and embedded in concrete slab. A total of 10 push-out tests were conducted under static loading to investigate the mechanical behavior of the proposed HST connector. The variables were the shapes (circular, square and rectangular) and sizes of hollow steel tubes, and the compressive strength of the concrete. The failure mode was recorded as: concrete slab compressive failure under the steel tube and concrete tensile splitting failure, where no failure occurred in the HST. Test results show that the square shape HST in filled via concrete strength 40 MPa carried the highest shear load value, showing three times more than the reference specimens. It also recorded less slip behavior, and less compressive failure mode in concrete underneath the square hollow connector in comparison with the circular and rectangular HST connectors in both concrete strengths. The rectangular HST shows a 20% higher shear resistance with a longer width in the load direction in comparison with that in the smaller dimension. The energy absorption capacity values showed 23% and 18% improvements with the square HST rather than a headed shear stud when embedded in concrete strengths of 25 MPa and 40 MPa, respectively. Moreover, an analytical method was proposed and predicts the shear resistance of the HST shear connectors with a standard deviation of 0.14 considering the shape and size of the connectors.

Evaluating the bond strength of FRP in concrete samples using machine learning methods

Abstract: In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

Teaching for quality learning at university

Abstract: by J. Biggs and C. Tang, Maidenhead, England, Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

Abstract: Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

Abstract: Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.

Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images:

Abstract: This study conducts crack identification from real-world images containing complicated disturbance information (cracks, handwriting scripts, and background) inside steel box girders of bridges. Con...