Azadeh Parvin

An experimental study on Kevlar strengthening of beam–column connections

Abstract This paper presents the results of an experimental investigation pertaining to the use of fiber-reinforced plastic (FRP) fabrics for strengthening of beam–column connections. Experimental studies were performed specifically for evaluating the moment capacity of the beam–column connections wrapped with Kevlar fiber composite fabric. Scaled down exterior beam–column connections were tested to examine the effect of various thickness and configurations of FRP fabrics applied to these connections. Experimental results demonstrate significant improvement of flexural capacity of beam column connections and provide certain guidelines for the proper FRP fabric thickness.

Concrete columns confined by fiber composite wraps under combined axial and cyclic lateral loads

This paper presents nonlinear finite element analysis of fiber reinforced polymer (FRP) jacketed reinforced concrete columns under combined axial and cyclic lateral loadings. Large-scale control and FRP-wrapped reinforced concrete columns (762 mm in diameter and 4978 mm in height) were modeled using the nonlinear finite element analysis software MARC™. The models were capable of allowing for the degradation of the stiffness under cyclic loading. The finite element analysis results indicated that reinforced concrete columns externally wrapped with the FRP fabric in the potential plastic hinge location at the bottom of the column showed significant improvement in both strength and ductility capacities, and the FRP jacket could be used to delay the degradation of the stiffness of reinforced concrete columns.

On the performance of Hopfield network for graph search problem

Abstract This paper presents a study on the performance of the Hopfield neural network algorithm for the graph path search problem. Specifically, performance of the Hopfield network is studied from the dynamic systems stability perspective. Simulations of the time behavior of the neural network is augmented with exhaustive stability analysis of the equilibrium points of the network dynamics. The goal is to understand the reasons for the well-known deficiency of the Hopfield network algorithm: the inability to scale up with the problem size. A recent procedure, which establishes solutions as stable equilibrium points in the state space of the network dynamics, is employed to define the constraint weight parameters of the Hopfield neural network. A simulation study of the network and stability analysis of equilibrium points indicate that a large set of non-solution equilibrium points also becomes stable whenever constraint weight parameters are set to make the solution equilibrium points stable. As the problem size grows, the number of stable non-solution equilibrium points increases at a much faster rate than the number of stable solution equilibrium points: the network becomes more likely to converge to a non-solution equilibrium point.

Fiber Reinforced Polymer Strengthening of Structures by Near-Surface Mounting Method

This paper provides a critical review of recent studies on strengthening of reinforced concrete and unreinforced masonry (URM) structures by fiber reinforced polymers (FRP) through near-surface mounting (NSM) method. The use of NSM-FRP has been on the rise, mainly due to composite materials’ high strength and stiffness, non-corrosive nature and ease of installation. Experimental investigations presented in this review have confirmed the benefits associated with NSM-FRP for flexural and shear strengthening of RC and URM structures. The use of prestressing and anchorage systems to further improve NSM-FRP strain utilization and changes in failure modes has also been presented. Bond behavior of NSM-FRP-concrete or masonry interface, which is a key factor in increasing the load capacity of RC and URM structures has been briefly explored. Presented studies related to the effect of temperature on the bond performance of NSM-FRP strengthened systems with various insulations and adhesive types, show better performance than externally bonded reinforcement (EBR) FRP retrofitting. In summary, the presented literature review provides an insight into the ongoing research on the use of NSM-FRP for strengthening of structural members and the trends for future research in this area.

Recurrent Neural Networks for Structural Optimization

This paper presents an improvement for an artificial neural network paradigm that has shown significant potential for successful application to a class of optimization problems in structural engineering. The artificial neural network paradigm includes algorithms that belong to the class of single-layer, relaxation-type recurrent neural networks. The suggested improvement enhances the convergence performance and involves a technique that sets the values of weight parameters of the recurrent neural network algorithm. The complete procedure of solving an optimization problem with a single-layer, relaxation-type recurrent neural network is introduced. The discrete Hopfield network is employed to solve the weighted matching problem. A set of simulation experiments is performed to analyze the performance of the discrete Hopfield network. Simulation results confirm that the discrete Hopfield network locates a locally optimal solution after each relaxation once the weight parameters are specified as defined in the suggested technique.

Determination of parameters in relaxation-search neural networks for optimization problems

We propose a method to define constraint weight parameters of the Hopfield network in order to establish the solutions of the optimization problem as stable equilibrium points in the state space. Application of the methodology is demonstrated on a well known benchmark problem, the traveling salesman problem. Simulation results indicate that the proposed bounds on the constraint weight parameters establish the solutions as stable points and consequently, the Hopfield network consistently converges to a solution after each relaxation.

Internal and External Reinforcement of Concrete Members by Use of Shape Memory Alloy and Fiber Reinforced Polymers under Cyclic Loading—A Review

This paper presents a review of recent studies on reinforced concrete (RC) structural components, such as beam-column joints (BCJs). These members are internally or externally reinforced with corrosion free shape memory alloy (SMA), fiber reinforced polymers (FRP), or a combination of the two materials. Bonded FRP sheets or near surface mounted (NSM) FRP bars are used in external strengthening cases. The use of FRP and SMA materials in RC structures can offer great potential benefits including lifetime cost saving, durability, safety, and post-earthquake serviceability for RC structures. Although FRP materials are well known for their corrosion resistance, high strength-to-weight ratios, ease of application, and constructability; SMA materials as reinforcement allow the structures to regain their original shape after the termination of the load without any permanent large residual deformation. In summary, the presented literature review provides an insight into the ongoing research on the use of these materials for retrofitting or strengthening of RC structural components and the trends for future research in this area. The cost and durability are also discussed.