Mahmoud Reda Taha

On and off-axis tension behavior of fiber reinforced polymer composites incorporating multi-walled carbon nanotubes

This investigation experimentally examines the role of multi-walled carbon nanotubes (MWCNTs) on the tension (on-axis tension test) and in-plane shear (off-axis tension test) behaviors of carbon fiber reinforced polymer composites. Both pristine and chemically functionalized MWCNTs were utilized with four different loadings: 0.1, 0.5, 1.0, and 1.5 wt% of epoxy. Our investigation showed that with 1.5 wt% functionalized MWCNTs, failure strain, ultimate strength, and toughness of the off-axis tension test are improved by 39%, 51%, and 121%, respectively. On the contrary, limited improvements were observed for the on-axis tested samples with functionalized or pristine MWCNTs.

NEW CONCRETE ANCHORS FOR CARBON FIBER-REINFORCED POLYMER POST-TENSIONING TENDONS--PART 1: STATE-OF-THE-ART REVIEW/DESIGN

In posttensioning applications, carbon fiber-reinforced polymer (CFRP) tendons tend to fail prematurely when used with anchors developed for steel tendons. A new non-metallic anchor for CFRP posttensioning tendons was developed to overcome the problem of premature failure and to provide a completely metal-free posttensioning system. The new anchors, as with conventional steel anchors, hold the tendons through mechanical gripping but without the corrugations between wedges and tendons. Each anchor consists of an outer barrel with a conical bore and 4 wedges. The anchor components are made of ultra-high-performance concrete (UHPC) developed specifically for this anchor, and the barrel is wrapped with CFRP sheets to provide the confinement necessary to fully utilize the strength and toughness of UHPC. The new concrete has compressive strength in excess of 200 MPa together with excellent durability and fracture toughness. Three mm chopped carbon fibers were incorporated into the UHPC to enhance fracture toughness. A review of the state-of-the-art of anchors for prestressing FRP tendons is introduced, followed by design of the new concrete anchors for posttensioned CFRP tendons.

A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data

Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage.

NEW CONCRETE ANCHORS FOR CARBON FIBER-REINFORCED POLYMER POST-TENSIONING TENDONS--PART 2: DEVELOPMENT/EXPERIMENTAL INVESTIGATION

A new nonmetallic anchor has been developed to avoid problems associated with metallic anchors used for fiber-reinforced polymer tendons. The anchor components are made of ultra high-performance concrete with compressive strength in excess of 200 MPa, developed specifically for this anchor. Special casting techniques were also developed to achieve the required concrete properties. The various production parameters of the new anchor were optimized through a sequential testing program. A multi-phase experimental program examining the anchor performance under both monotonic and cyclic loading is presented. The concrete anchor showed excellent mechanical performance by passing the acceptable short-term and fatigue requirements.

Homogenization Model Examining the Effect of Nanosilica on Concrete Strength and Stiffness

Composite homogenization is a numerical simulation method that allows determination of the effect of microstructure constituents on the mechanical properties of composite materials. This homogenization technique allows cement paste to be modeled as a composite material in which microparticles are randomly dispersed in the cement paste matrix. By using microstructural homogenization, a representative volume element (RVE) can be developed and used to simulate the constitutive model of the composite cement paste by considering its constituent phases. A four-phase cement paste model is considered to describe cement paste incorporating nanosilica. Cement paste microstructures are divided into Phase I, hydrated cement paste; Phase II, unhydrated cement paste; Phase III, nonreacted nanosilica; and Phase IV, capillary porosity. Cement hydration models are used to predict the volume fraction of the four phases on the basis of the mixture proportion of the cement paste mix. Constitutive models for Phases I, III, and IV are assumed on the basis the literature. A constitutive model for Phase II is identified with the RVE model by matching the stress–strain curves of the cement paste extracted from nanoindentation experiments. The validated RVE model is then used to examine the effect of changing the nanosilica content in the cement paste on the stress–strain curve of the composite cement paste. It is evident computationally that increasing the nanosilica content will increase the strength and stiffness of the cement paste and therefore will increase the ability of the cement paste to absorb energy represented by the area under the stress–strain curve.

Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation

Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.

Rheological modelling of masonry creepThis article is one of a selection of papers published in this Special Issue on Masonry.

In the present study, masonry creep was experimentally investigated. Creep tests were performed on masonry prisms, which were produced using standard fired clay brick and standard Type S mortar. A total of 11 sets of loaded and unloaded masonry specimens were tested under sustained load with three main parameters: stress level, masonry age at loading, and relative humidity. The unloaded prisms compensated for the effects of shrinkage. In this article, the ability of a number of rheological models reported in the literature are examined for their ability to predict masonry creep. Moreover, a new rheological model, one that considers the effect of stress level and masonry age at loading, is proposed. The system parameters of the proposed model were identified using the experimental data. The proposed model was then validated using masonry creep data that was reported by other researchers, but not used in model development. It is shown that the creep behaviour of masonry can be modelled with good accuracy us...

Simplified Punching Shear Design Method for Slab-Column Connections Using Fuzzy Learning

This paper presents a new approach for predicting the punching shear strength of concentrically loaded interior slab-column connections using fuzzy learning. A total of 178 experimental datasets obtained from concentric punching shear tests of reinforced concrete slab-column connections from the literature are used in training and testing of the fuzzy system. The fuzzy-based model is developed to address the interaction between various punching shear modeling parameters and the uncertainties between them, which might not be properly captured in classical modeling approaches. The model is trained using 82 datasets and verified using 96 datasets that are not used in the training process. The punching shear strength predicted by the fuzzy-based model is compared with those predicted by current punching shear strength models widely used in the design practice. The fuzzy-based model demonstrates a higher prediction of the punching shear strength of concentrically loaded interior slab-column connections than all current design codes. It also respects the fundamental failure mechanics of punching shear that have been observed by previous researchers. The authors also propose a simplified design model based on a set of design charts that were developed from the fuzzy-based model.

Numerical Investigation of Creep Effects on FRP-Strengthened RC Beams

Numerical analysis using a finite-element model was performed to simulate and investigate the long-term behavior of two RC beams with similar steel reinforcement, cast from the same batch of concrete. One beam was a plain RC beam and the other beam was strengthened using carbon fiber-reinforced polymer (FRP) strips. The deflections of both beams have been monitored for 5 years after loading. The finite-element model included both creep of concrete and viscoelasticity of the epoxy adhesive at the concrete-carbon FRP (CFRP) interface. The results of the finite-element analysis are compared to experimental observations of the two beams. The finite-element analysis was found to be able to simulate the long-term behavior of the CFRP-strengthened beam and help us understand the complex changes in the stress state that occur over time.

Exploratory Investigations for Intelligent Damage Prognosis using Hidden Markov Models

Structural damage occurs due to structural overloading or due to environmental conditions or combined effects. Extensive research on structural health monitoring, damage diagnosis and damage pattern recognition has been developed over the last two decades. Recently, damage prognosis has become a new research focus that tries to use damage diagnosis knowledge to inform the responsible authorities on the expected remaining service life of the structure such that efficient maintenance operations can be scheduled. We demonstrate here the use of hidden Markov models to model damage accumulation and propagation in structures and to predict the residual service life of the structure given damage diagnosis observations. The proposed model is described and is tested using damage diagnosis simulated data of a pre-stressed concrete bridge