Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Seismic fragility curves represent likelihood of structures meeting various damage stages. Epistemic as well as aleatory uncertainties associated with seismic loads and structural behaviors are usually taken into account in order to analytically develop such curves. Such structural analyses are time-consuming, demanding extensive computational efforts. In this study, in order to reduce this endeavor, artificial neural network method is applied to develop structural seismic fragility curves under collapse damage state, considering effects of record-to-record variability and modeling parameter uncertainties. Structural analyses are performed for a limited number of scenarios of structures under a limited number of recorded strong ground motion records. Probability distribution for each modeling parameter was used to simulate each structure scenario. Incremental dynamic analysis was used to assess spectral acceleration associated with collapse limit state for each structure scenario. The results of the analyses were used to train and validate a three-layered artificial neural network, and Monte Carlo simulation is implemented based on trained neural network for a sample moment-resisting steel frame in order to derive collapse fragility curve. Application of the proposed method enhances accuracy of identical computational run time compared with response surface–based method.

Abstract

Abstract

Abstract

The objective of this paper is to develop a more accurate and reliable alternative method using fuzzy inference system (FIS) to predict the shear strength of FRP-reinforced concrete beams. Such an accurate model, which can lead to an economical use of FRP reinforcement, is in high demand since existing design provisions for shear capacity of FRP-RC beams are either very conservative or even inadequate mainly due to two factors. Firstly, the current design codes follow the conventional assumption of superposition of concrete plus stirrup contribution to the shear strength, hence ignoring any interaction between shear resisting mechanisms. Secondly, the current design guidelines simply assume that some modified versions of the shear design equations which are empirically derived for steel-reinforced concrete beams can be easily extended to cover FRP-RC beams although the guidelines vary greatly in the manner they modify the equations. Given very different properties of FRP as compared to those of steel, such an assumption, however, needs to be examined and validated. To relax both of these assumptions, the FIS approach offers an attractive solution because it does not require a priori information. As a result, the proposed FIS model compares favorably with a large data base containing the test results of 197 FRP-RC beams assembled from literature. Moreover, the proposed FIS model outperforms the latest design provisions for shear strength of FRP-RC beams, namely ACI 440-06 and CSA S806-02. Also, a special attention is paid to differentiate between shear-compression mode and shear-tension mode of failure, which are the two common types of shear failure for FRP-RC beams with FRP stirrups. In light of the proposed FIS model, modifications to the shear-compression resistance provided by the considered design guidelines are recommended.

Abstract

The application of fiber-reinforced polymer (FRP) strips or rods in the form of near-surface-mounted (NSM) reinforcement has become an attractive solution to strengthen the existing buildings and bridges. It is of interest to engineers to have an accurate estimate of the bond capacity of this technique. In this paper, fuzzy logic approach is utilized to propose an alternative method of determining the pullout strength of NSM FRP strips/rods which are bonded to the concrete block. Two types of fuzzy logic models, namely Mamdani and Takagi–Sugeno, are developed. With the aim of enhancing the interpretability of the fuzzy model, the rule base of Mamdani model is extracted from the classification decision tree, and the membership functions corresponding to the linguistic concepts are built by uniform partitioning the range of variables. On the other hand, in order to arrive at closed-form equations for pullout capacity, the subtractive clustering algorithm is employed to deduce the rule base and membership functions of Takagi–Sugeno model (first order), and its consequent part is tuned by the least square optimization using training dataset. Several fuzzy logic models of both types with different numbers of rules are developed and compared in terms of different error measures. To train and validate the fuzzy models, a large database of 384 direct pullout tests on NSM FRP bonded to concrete is assembled from the literature. The results reveal that both of the proposed Mamdani and Takagi–Sugeno models demonstrate good accuracy against the experimental data and outperform the published models. A parametric study indicates that the proposed fuzzy models can predict the maximum effective bond length, and thus, they are able to capture the underlying mechanics of the problem.

Abstract

In this paper, fuzzy inference system (FIS) is employed to develop a more accurate approach to evaluate the strength and strain capacity of axially loaded concrete columns with the square section confined by fiber-reinforced polymer (FRP) wraps. To do so, an experimental database containing 261 test data on compressive strength and 112 test data on ultimate strain is collated from the literature. By using subtractive clustering algorithm to extract cluster centers from the experimental database, the structure of FIS model is identified. To select the best FIS model, several constant and linear (i.e., zeroth- and first-order) Takagi–Sugeno FIS models with different numbers of rules are developed and their performances in terms of the model output errors with respect to training data set as well as validation data set are compared. The finally proposed FIS models for calculation of strength and strain contain as few as three rules. Besides, the proposed FIS models are expressed as closed-form formulations, which can be conveniently used in practice. The outputs of the proposed FIS models agree favorably with the test data and outperform the existing models by providing more accurate prediction of both strength and strain capacity. In view of the FIS models, a parametric study is carried out to examine the influence of various variables including the section corner radius as well as the elastic modulus and tensile strength of FRP on the capacity of FRP-confined square columns.