Experimental investigation of load distribution in a composite girder bridge at elastic versus inelastic states

Bridge design and evaluation involve the determination of the internal forces and moments that each bridge element must resist. In slab-on-girder bridges, the moment and shear caused by traffic loads are normally determined using load distribution factors. These factors are derived based on results of analytical models, numerical analyses, as well as actual loading tests, but there appears to be scant experimental data to gauge their accuracy, particularly beyond the elastic limit state. To address the scarcity of the experimental data and to understand how the distribution characteristics of concrete slab on steel girder composite bridges change with the advent of yielding and inelasticity, a 1/3 scale model of a hypothetical composite bridge was tested to failure in this study.Extensive measurements were taken during the test to allow better understanding of the response of slab-on-girder bridges as well as their live load distribution characteristics at all stages of loading up to failure. The experimentally determined distribution factors for the tested bridge model are compared with the calculated values based on the Canadian Highway Bridge Design Standard, and the code values are found to overestimate the maximum moment in the interior loaded girder by about 22% and 33% at the elastic and the inelastic states, respectively. \ua9 2012 Elsevier Ltd.Peer reviewed: YesNRC publication: Ye

Nonlinear behaviour of steel-concrete composite bridges: Finite element modelling and experimental verification

To ensure public safety, existing bridges are often evaluated for their load-bearing capacity. Realistic evaluation must take into account the actual nonlinear stress-strain characteristics of the bridge materials and the interaction among its structural components at all stages of loading up to failure. The finite element method is particularly suitable for this type of analysis, but nonlinear finite element formulations involve many assumptions that must be verified before any practical application. In this study, the accuracy of a nonlinear finite element program developed by the authors for the analysis of composite steel-concrete bridges is checked by comparing its results with experimental data from simply supported and continuous beams tested by others and from a 1/3 scale multi-girder composite bridge tested by the authors. Good agreement is observed between the measured and the computed load-deflection responses and strains at all stages of loading up to the maximum load, which demonstrates the accuracy of the finite element formulation and the validity of its assumptions.Peer reviewed: YesNRC publication: Ye

Improving the energy efficiency of buildings with hollow core slabs: A numerical investigation

Thermal mass is the capacity of a material to store heat. Concrete or masonry has a higher heat storage capacity than air; therefore, there is significant potential in using the natural thermal mass of buildings to reduce and to shift peak load energy demands. Most residential and commercial buildings have adequate thermal mass that can be utilized to reduce and shift peak energy load. In particular, hollow core slabs that utilize air passing through the slabs to transfer heat in and out of concrete, have the potential to reduce and to shift peak load requirements. This paper presents a numerical investigation that aims to investigate design parameters of hollow core slabs for the maximum energy efficiency, particularly with respect to peak energy demand reduction and shifting. Results reveal that hollow core slab system can be actively used to improve the energy efficiency of buildings. The use of phase change materials (PCM) along with the thermal mass of hollow core slabs enhances both peak load reduction and phase shift; therefore, composite systems that combine the thermal mass of concrete with PCMs emerge as feasible design alternatives to commonly used flat slab systems

Bridge management by dynamic programming and neural networks

Bridges and pavements represent the major investment in a highway network. In addition, they are in constant need of maintenance, rehabilitation, and replacement. One of the problems related to highway infrastructure is that the cost of maintaining a network of bridges with an acceptable level-of-service is more than the budgeted funds. For large bridge networks, traditional management practices have become inadequate for dealing with this serious problem. Bridge management systems are a relatively new approach developed to solve the latter problem, following the successful application of similar system concepts to pavement management. Priority setting schemes used in bridge management systems range from subjective basis using engineering judgement to very complex optimization models. However, currently used priority setting schemes do not have the ability to optimize the system benefits in order to get optimal solutions. This paper presents a network optimization model which allocates a limited budget to bridge projects. The objective of the model is to determine the best timing for carrying out these projects and the spending level for each year of the analysis period in order to minimize the losses of the system benefits. A combined dynamic programming and neural network approach was utilized to formulate the model. The bridge problem has two dimensions: the time dimension and the bridge network dimension. The dynamic programming sets its stages in the time dimension, while the neural network handles the network dimension