Modeling shear capacity of RC slender beams without stirrups using genetic algorithms

Nehdi, M.,Greenough, T. Techno-Press 2007 Smart Structures and Systems, An International Jou Vol.3 No.1

High-strength concrete (HSC) is becoming increasingly attractive for various construction projects since it offers a multitude of benefits over normal-strength concrete (NSC). Unfortunately, current design provisions for shear capacity of RC slender beams are generally based on data developed for NSC members having a compressive strength of up to 50 MPa, with limited recommendations on the use of HSC. The failure of HSC beams is noticeably different than that of NSC beams since the transition zone between the cement paste and aggregates is much denser in HSC. Thus, unlike NSC beams in which micro-cracks propagate around aggregates, providing significant aggregate interlock, micro-cracks in HSC are trans-granular, resulting in relatively smoother fracture surfaces, thereby inhibiting aggregate interlock as a shear transfer mechanism and reducing the influence of compressive strength on the ultimate shear strength of HSC beams. In this study, a new approach based on genetic algorithms (GAs) was used to predict the shear capacity of both NSC and HSC slender beams without shear reinforcement. Shear capacity predictions of the GA model were compared to calculations of four other commonly used methods: the ACI method, CSA method, Eurocode-2, and Zsutty's equation. A parametric study was conducted to evaluate the ability of the GA model to capture the effect of basic shear design parameters on the behaviour of reinforced concrete (RC) beams under shear loading. The parameters investigated include compressivestrength, amount of longitudinal reinforcement, and beam's depth. It was found that the GA model provided more accurate evaluation of shear capacity compared to that of the other common methods and better captured the influence of the significant shear design parameters. Therefore, the GA model offers an attractive user-friendly alternative to conventional shear design methods.

Seismic behaviour of repaired superelastic shape memory alloy reinforced concrete beam-column joint

Nehdi, Moncef,Alam, M. Shahria,Youssef, Maged A. Techno-Press 2011 Smart Structures and Systems, An International Jou Vol.7 No.5

Large-scale earthquakes pose serious threats to infrastructure causing substantial damage and large residual deformations. Superelastic (SE) Shape-Memory-Alloys (SMAs) are unique alloys with the ability to undergo large deformations, but can recover its original shape upon stress removal. The purpose of this research is to exploit this characteristic of SMAs such that concrete Beam-Column Joints (BCJs) reinforced with SMA bars at the plastic hinge region experience reduced residual deformation at the end of earthquakes. Another objective is to evaluate the seismic performance of SMA Reinforced Concrete BCJs repaired with flowable Structural-Repair-Concrete (SRC). A $\frac{3}{4}$-scale BCJ reinforced with SMA rebars in the plastic-hinge zone was tested under reversed cyclic loading, and subsequently repaired and retested. The joint was selected from an RC building located in the seismic region of western Canada. It was designed and detailed according to the NBCC 2005 and CSA A23.3-04 recommendations. The behaviour under reversed cyclic loading of the original and repaired joints, their load-storey drift, and energy dissipation ability were compared. The results demonstrate that SMA-RC BCJs are able to recover nearly all of their post-yield deformation, requiring a minimum amount of repair, even after a large earthquake, proving to be smart structural elements. It was also shown that the use of SRC to repair damaged BCJs can restore its full capacity.

Evaluation of shear capacity of FRP reinforced concrete beams using artificial neural networks

Nehdi, M.,El Chabib, H.,Said, A. Techno-Press 2006 Smart Structures and Systems, An International Jou Vol.2 No.1

To calculate the shear capacity of concrete beams reinforced with fibre-reinforced polymer (FRP), current shear design provisions use slightly modified versions of existing semi-empirical shear design equations that were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and mode of failure than steel, and extending existing shear design equations for steel reinforced beams to cover concrete beams reinforced with FRP is questionable. This paper investigates the feasibility of using artificial neural networks (ANNs) to estimate the nominal shear capacity, Vn of concrete beams reinforced with FRP bars. Experimental data on 150 FRP-reinforced beams were retrieved from published literature. The resulting database was used to evaluate the validity of several existing shear design methods for FRP reinforced beams, namely the ACI 440-03, CSA S806-02, JSCE-97, and ISIS Canada-01. The database was also used to develop an ANN model to predict the shear capacity of FRP reinforced concrete beams. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP reinforced concrete beams. Based on ANN predictions, modified equations are proposed for the shear design of FRP reinforced concrete beams and proved to be more accurate than existing equations.

Modeling shear capacity of RC slender beams without stirrups using genetic algorithms

M. Nehdi,T. Greenough 국제구조공학회 2007 Smart Structures and Systems, An International Jou Vol.3 No.1

High-strength concrete (HSC) is becoming increasingly attractive for various construction projects since it offers a multitude of benefits over normal-strength concrete (NSC). Unfortunately, current design provisions for shear capacity of RC slender beams are generally based on data developed for NSC members having a compressive strength of up to 50 MPa, with limited recommendations on the use of HSC. The failure of HSC beams is noticeably different than that of NSC beams since the transition zone between the cement paste and aggregates is much denser in HSC. Thus, unlike NSC beams in which micro-cracks propagate around aggregates, providing significant aggregate interlock, micro-cracks in HSC are trans-granular, resulting in relatively smoother fracture surfaces, thereby inhibiting aggregate interlock as a shear transfer mechanism and reducing the influence of compressive strength on the ultimate shear strength of HSC beams. In this study, a new approach based on genetic algorithms (GAs) was used to predict the shear capacity of both NSC and HSC slender beams without shear reinforcement. Shear capacity predictions of the GA model were compared to calculations of four other commonly used methods: the ACI method, CSA method, Eurocode-2, and Zsutty equation. A parametric study was conducted to evaluate the ability of the GA model to capture the effect of basic shear design parameters on the behaviour of reinforced concrete (RC) beams under shear loading. The parameters investigated include compressive strength, amount of longitudinal reinforcement, and beam depth. It was found that the GA model provided more accurate evaluation of shear capacity compared to that of the other common methods and better captured the influence of the significant shear design parameters. Therefore, the GA model offers an attractive user-friendly alternative to conventional shear design methods.

Seismic behaviour of repaired superelastic shape memory alloy reinforced concrete beam-column joint

Moncef Nehdi,M. Shahria Alam,Maged A. Youssef 국제구조공학회 2011 Smart Structures and Systems, An International Jou Vol.7 No.5

Large-scale earthquakes pose serious threats to infrastructure causing substantial damage and large residual deformations. Superelastic (SE) Shape-Memory-Alloys (SMAs) are unique alloys with the ability to undergo large deformations, but can recover its original shape upon stress removal. The purpose of this research is to exploit this characteristic of SMAs such that concrete Beam-Column Joints (BCJs) reinforced with SMA bars at the plastic hinge region experience reduced residual deformation at the end of earthquakes. Another objective is to evaluate the seismic performance of SMA Reinforced Concrete BCJs repaired with flowable Structural-Repair-Concrete (SRC). A ¾-scale BCJ reinforced with SMA rebars in the plastic-hinge zone was tested under reversed cyclic loading, and subsequently repaired and retested. The joint was selected from an RC building located in the seismic region of western Canada. It was designed and detailed according to the NBCC 2005 and CSA A23.3-04 recommendations. The behaviour under reversed cyclic loading of the original and repaired joints, their load-storey drift, and energy dissipation ability were compared. The results demonstrate that SMA-RC BCJs are able to recover nearly all of their post-yield deformation, requiring a minimum amount of repair, even after a large earthquake, proving to be smart structural elements. It was also shown that the use of SRC to repair damaged BCJs can restore its full capacity.

Evaluation of shear capacity of FRP reinforced concrete beams using artficial neural networks

M. Nehdi,H. El Chabib,A. Saïd 국제구조공학회 2006 Smart Structures and Systems, An International Jou Vol.2 No.1

To calculate the shear capacity of concrete beams reinforced with fibre-reinforced polymer (FRP), current shear design provisions use slightly modified versions of existing semi-empirical shear design equations that were primarily derived from experimental data generated on concrete beams having steel reinforcement. However, FRP materials have different mechanical properties and mode of failure than steel, and extending existing shear design equations for steel reinforced beams to cover concrete beams reinforced with FRP is questionable. This paper investigates the feasibility of using artificial neural networks (ANNs) to estimate the nominal shear capacity, Vn of concrete beams reinforced with FRP bars. Experimental data on 150 FRP-reinforced beams were retrieved from published literature. The resulting database was used to evaluate the validity of several existing shear design methods for FRP reinforced beams, namely the ACI 440-03, CSA S806-02, JSCE-97, and ISIS Canada-01. The database was also used to develop an ANN model to predict the shear capacity of FRP reinforced concrete beams. Results show that current guidelines are either inadequate or very conservative in estimating the shear strength of FRP reinforced concrete beams. Based on ANN predictions, modified equations are proposed for the shear design of FRP reinforced concrete beams and proved to be more accurate than existing equations.

Ultra-High Performance Concrete: Mechanical Performance, Durability, Sustainability and Implementation Challenges

Abbas, S.,Nehdi, M.L.,Saleem, M.A. Korea Concrete Institute 2016 International Journal of Concrete Structures and M Vol.10 No.3

In this study, an extensive literature review has been conducted on the material characterization of UHPC and its potential for large-scale field applicability. The successful production of ultra-high performance concrete (UHPC) depends on its material ingredients and mixture proportioning, which leads to denser and relatively more homogenous particle packing. A database was compiled from various research and field studies around the world on the mechanical and durability performance of UHPC. It is shown that UHPC provides a viable and long-term solution for improved sustainable construction owing to its ultrahigh strength properties, improved fatigue behavior and very low porosity, leading to excellent resistance against aggressive environments. The literature review revealed that the curing regimes and fiber dosage are the main factors that control the mechanical and durability properties of UHPC. Currently, the applications of UHPC in construction are very limited due to its higher initial cost, lack of contractor experience and the absence of widely accepted design provisions. However, sustained research progress in producing UHPC using locally available materials under normal curing conditions should reduce its material cost. Current challenges regarding the implementation of UHPC in full-scale structures are highlighted. This study strives to assist engineers, consultants, contractors and other construction industry stakeholders to better understand the unique characteristics and capabilities of UHPC, which should demystify this resilient and sustainable construction material.

Shape memory alloy-based smart RC bridges: overview of state-of-the-art

Alam, M.S.,Nehdi, M.,Youssef, M.A. Techno-Press 2008 Smart Structures and Systems, An International Jou Vol.4 No.3

Shape Memory Alloys (SMAs) are unique materials with a paramount potential for various applications in bridges. The novelty of this material lies in its ability to undergo large deformations and return to its undeformed shape through stress removal (superelasticity) or heating (shape memory effect). In particular, Ni-Ti alloys have distinct thermomechanical properties including superelasticity, shape memory effect, and hysteretic damping. SMA along with sensing devices can be effectively used to construct smart Reinforced Concrete (RC) bridges that can detect and repair damage, and adapt to changes in the loading conditions. SMA can also be used to retrofit existing deficient bridges. This includes the use of external post-tensioning, dampers, isolators and/or restrainers. This paper critically examines the fundamental characteristics of SMA and available sensing devices emphasizing the factors that control their properties. Existing SMA models are discussed and the application of one of the models to analyze a bridge pier is presented. SMA applications in the construction of smart bridge structures are discussed. Future trends and methods to achieve smart bridges are also proposed.

Predicting shear capacity of NSC and HSC slender beams without stirrups using artificial intelligence

H. El-Chabib,M. Nehdi,A. Said 한국계산역학회 2005 Computers and Concrete, An International Journal Vol.2 No.1

The use of high-strength concrete (HSC) has significantly increased over the last decade, especially in offshore structures, long-span bridges, and tall buildings. The behavior of such concrete is noticeably different from that of normal-strength concrete (NSC) due to its different microstructure and mode of failure. In particular, the shear capacity of structural members made of HSC is a concern and must be carefully evaluated. The shear fracture surface in HSC members is usually trans-granular (propagates across coarse aggregates) and is therefore smoother than that in NSC members, which reduces the effect of shear transfer mechanisms through aggregate interlock across cracks, thus reducing the ultimate shear strength. Current code provisions for shear design are mainly based on experimental results obtained on NSC members having compressive strength of up to 50MPa. The validity of such methods to calculate the shear strength of HSC members is still questionable. In this study, a new approach based on artificial neural networks (ANNs) was used to predict the shear capacity of NSC and HSC beams without shear reinforcement. Shear capacities predicted by the ANN model were compared to those of five other methods commonly used in shear investigations: the ACI method, the CSA simplified method, Response 2000, Eurocode-2, and Zsutty's method. A sensitivity analysis was conducted to evaluate the ability of ANNs to capture the effect of main shear design parameters (concrete compressive strength, amount of longitudinal reinforcement, beam size, and shear span to depth ratio) on the shear capacity of reinforced NSC and HSC beams. It was found that the ANN model outperformed all other considered methods, providing more accurate results of shear capacity, and better capturing the effect of basic shear design parameters. Therefore, it offers an efficient alternative to evaluate the shear capacity of NSC and HSC members without stirrups.

Genetic algorithm-based yield stress equations for concrete at high temperature and prolonged mixing time

S. Al Martini,M. Nehdi 사단법인 한국계산역학회 2009 Computers and Concrete, An International Journal Vol.6 No.4

Experiments were designed to investigate the flow behavior of portland cement paste and concrete incorporating superplasticizers. The paste and concrete mixtures were subjected to prolonged mixing for up to 110 min at high temperature. The yield stress values of concrete and that of the corresponding cement paste were measured using a rotating rheometer and viscometer, respectively. The results reveal a weak linear correlation between the yield stress of concrete mixtures and that of the corresponding cement pastes. Results also indicate that the yield stress of concrete varies in a linear fashion with the elapsed time, while its variations with the temperature and superplasticizer dosage follow power and inverse power functions, respectively. In this study, the genetic algorithms (GA) technique was used to predict the yield stress of concrete considering various parameters, such as the mixing time, ambient temperature, and superplasticizer dosage. A sensitivity study was conducted to evaluate the ability of the GA equations thus developed to capture the effects of test parameters on the yield stress of concrete. It was found that the GA equations were sensitive to the effects of test parameters and provided yield stress predictions that compared well with corresponding experimental data.