Maurizio Guadagnini

Mechanical Characterization of Basalt FRP Rebars and Long-Term Strength Predictive Model

AbstractThe use of basalt fiber–reinforced polymers (BFRP) in construction applications is relatively new and, although its mechanical performance is expected to be similar to that of glass fiber–reinforced polymer, not many studies have addressed its performance in concrete and mortar environments. This paper examines the degradation of BFRP bars at their product development stage after exposure to accelerated environmental conditions and proposes a methodology to predict their long-term design strength. A total of 132 BFRP specimens comprising two types and seven different diameters were tested in tension after conditioning in pH9 and pH13 solutions at 20, 40, and 60°C for 100; 200; 1,000; and 5,000 h. Based on the results obtained and adopting the durability approach of industry standards for FRP reinforcement in concrete structures, a comprehensive long-term strength predictive model for fiber-reinforced polymer (FRP) bars in multiple environments is proposed and exemplified. The BFRP bars tested as p...

Full-Scale Shaking Table Tests on a Substandard RC Building Repaired and Strengthened with Post-Tensioned Metal Straps

The effectiveness of a novel Post-Tensioned Metal Strapping (PTMS) technique at enhancing the seismic behavior of a substandard RC building was investigated through full-scale, shake-table tests during the EU-funded project BANDIT. The building had inadequate reinforcement detailing in columns and joints to replicate old construction practices. After the bare building was initially damaged significantly, it was repaired and strengthened with PTMS to perform additional seismic tests. The PTMS technique improved considerably the seismic performance of the tested building. While the bare building experienced critical damage at an earthquake of PGA = 0.15 g, the PTMS-strengthened building sustained a PGA = 0.35 g earthquake without compromising stability.

Shear Performance of FRP Reinforced Concrete Beams

The paper deals with shear resistance of RC beams reinforced with fibre reinforced polymer reinforcement. The experimental programme presented here is part of an extensive research activity focused on comparing the development of shear carrying mechanisms in concrete beams reinforced with either steel or FRP reinforcement. Two sets of beams with equivalent geometrical characteristics were examined. Three beams were reinforced in flexure with conventional longitudinal steel bars and three beams with equivalent glass FRP bars. Shear reinforcement was provided in the form of transverse external links manufactured using glass fibres for the first set of beams and carbon fibres for the second. The experimental programme is described and findings of the project are reported and discussed in the context of the development of design guidelines urgently needed to support the wider use of FRP reinforcement for structural concrete applications.

Seismic Strengthening of Severely Damaged Beam-Column RC Joints Using CFRP

This article investigates the seismic behavior of three full-scale exterior reinforced concrete (RC) beam-column joints rehabilitated and strengthened with externally bonded carbon fiber–reinforced polymers (CFRP). The specimens had inadequate detailing in the core zone and replicated joints of a real substandard building tested as part of the EU-funded project BANDIT. Seven tests were performed in two successive phases. The bare joints were first subjected to reversed cyclic loading tests to assess their basic seismic performance. As these initial tests produced severe damage in the core, the damaged concrete was replaced with new high-strength concrete. The specimens were subsequently strengthened with CFRP sheets and the cyclic tests were repeated. The results indicate that the core replacement with new concrete enhanced the shear strength of the substandard joints by up to 44% over the bare counterparts. ASCE guidelines predict accurately the shear strength of the bare and rehabilitated joints. The CFRP strengthening enhanced further the joint strength by up to 69%, achieving a shear strength comparable to that of joints designed according to modern seismic provisions. Therefore, the rehabilitation/strengthening method is very effective for postearthquake strengthening of typical substandard structures of developing countries.

Serviceability Limit State of FRP RC Beams

Owing to the particular mechanical properties of FRPs, the design of Fiber Reinforced Polymer (FRP) Reinforced Concrete (RC) structures is often governed by serviceability requirements, rather than ultimate capacity. The low stiffness of FRPs generally results in large strains being mobilized already at low levels of externally applied load, and in turn can lead to significant crack widths and deflections. This paper reviews and discusses the serviceability limitations inherent in current design codes and guidelines in terms of stress limitation, cracking and deflection control. The predictions obtained in accordance to these code equations, as well as other existing proposals for the design of FRP RC structures, are then compared to the results of an experimental program on 24 GFRP RC beams tested under four-point load.

Predicting the Shear Strength of RC Beams Without Stirrups Using Bayesian Neural Network

This paper presents the application of Bayesian learning to train a multi layer perceptron network on experimental test on Reinforced Concrete (RC) beams without stirrups failing in shear. The trained network was found to provide good estimate of shear strength when the input variables (i.e. shear parameters) are within the range in the experimental database used for training. Within the Bayesian framework, a process known as the Automatic Relevance Determination is employed to assess the relative importance of different input variables on the output (i.e. shear strength). Finally the network is utilised to simulate typical RC beams failing in shear. Advances in neural computing have shown that a neural learning approach that uses Bayesian inference can essentially eliminate the problem of over fitting, which is common with conventional back propagation Neural Networks. In addition, Bayesian Neural Network can provide the confidence (error) associated with its prediction.

Bend Strength of FRP Bars: Experimental Investigation and Bond Modeling

AbstractThe unique properties of internal fiber-reinforced polymer (FRP) reinforcement influence their bond interaction with the surrounding concrete. This is especially true in the case of bent FR...

Numerical Investigation of the Shear Strength of RC Deep Beams Using the Microplane Model

AbstractAlthough much work has been done on the shear behavior of RC elements, current design provisions are still based on empirical data and their predictions, especially for deep beams, are not always reliable and can lead to unconservative results. This paper presents an extensive numerical investigation on the role of key parameters on the shear performance of RC deep beams using the microplane M4 material model. The model is validated against experimental results of 20 RC deep beams. A parametric study is then carried out to investigate the effect of shear span to depth ratio and concrete compressive strength for RC deep beams with and without shear reinforcement. Although a single strut mechanism is generally mobilized in deep beams, the presence of shear reinforcement can enable a more uniform distribution of shear stresses within the shear span and enhance the effectiveness of concrete cracked in tension. The study confirms that both shear span to depth ratio and concrete strength are the key par...

Multi-level nonlinear modeling verification scheme of RC high-rise wall buildings

Earthquake-resistant reinforced concrete (RC) high-rise wall buildings are designed and detailed to respond well beyond the elastic range under the expected earthquake ground motions. However, despite their considerable section depth, in terms of analysis, RC walls are still often treated as linear elements, ignoring the effect of deformation compatibility. Due to the limited number of available comprehensive experimental studies on RC structural wall systems subjected to cycling loading, few in-depth analytical verification studies have been conducted. Motivated by the increasing need for more accurate seismic risk assessment of high-rise buildings in multi-scenario seismic regions, a multi-level nonlinear modeling verification scheme is presented in this paper to investigate two different nonlinear modeling techniques for shear walls (2- and 4-noded fiber-based elements). The investigated modeling approaches and their key parameters are verified against the results of Phase I of uniaxial shaking table specimen tests (performed at the University of California, San Diego) on a seven-story full-scale RC shear wall structure under base excitations representing four earthquake records of increasing intensities. Three numerical models are created using two different tools (ZEUS-NL and PERFORM-3D). The results obtained from the numerical models are compared with the experimental results both on global and local response levels (top displacement, interstory drift, story shear force, story bending moment, period elongation and rebar tensile strain). The study reveals the superior performance of 4-noded fiber-based wall/shell element modeling approach in accounting for the 3D effects of deformation compatibility between lateral and gravity-force-resisting systems. The study also highlights the sensitivity of attained results to the stiffnesses assigned to the rigid links and 3D joints required to connect the shear walls to neighboring elements when a 2-noded element is used.

Predicting the shear resistance of RC beams without shear reinforcement using a Bayesian neural network

Advances in neural computing have shown that a neural learning approach that uses Bayesian inference can essentially eliminate the problem of over fitting, which is common with conventional back-propagation neural networks. In addition, Bayesian neural network can provide the confidence (error) associated with its prediction. This paper presents the application of Bayesian learning to train a multilayer perceptron network to predict the shear resistance of reinforced concrete beams without shear reinforcement. The automatic relevance determination technique was employed to assess the relative importance of the different input variables considered in this study on the shear resistance of reinforced concrete beams. The performance of the Bayesian neural network is examined and discussed along with that of current shear design provisions.