Simplified Formula for Axial Strains of Buried Pipes Induced by Propagating Seismic Waves

Pipe strains developed in buried straight pipes by horizontally propagating seismic waves are analyzed. Extensive discussion is made for the general slippage conditions between soils and pipes, as well as for the arbitrary angle of incidence of the longitudinal and transverse waves relative to the pipe axis. After the pipe strain solutions and their upper and lower bounds are obtained for the given values of the angle of incidence, solutions for the maximum pipe strains with unknown angles of incidence are discussed. In particular, simple approximate closed-form solutions for the maximum pipe strains developed herein should be useful for practical applications

Structural Reliability Estimation with Vibration-Based Identified Parameters

This paper presents a unique structural reliability estimation method incorporating structural parameter identification results based on the seismic response measurement. In the shaking table test, a three-bent concrete bridge model was shaken to different damage levels by a sequence of earthquake motions with increasing intensities. Structural parameters, stiffness and damping values of the bridge were identified under damaging seismic events based on the seismic response measurement. A methodology was developed to understand the importance of structural parameter identification in the reliability estimation. Along this line, a set of structural parameters were generated based on the Monte Carlo simulation. Each of them was assigned to the base bridge model. Then, every bridge model was analyzed using nonlinear time history analyses to obtain damage level at the specific locations. Last, reliability estimation was performed for bridges modeled with two sets of structural parameters. The first one was obtained by the nonlinear time history analysis with the Monte Carlo simulated parameters which is called nonupdated structural parameters. The second one was obtained by updating the first set in Bayesian sense based on the vibration-based identification results which is called updated structural parameters. In the scope of this paper, it was shown that residual reliability of the system estimated using the updated structural parameters is lower than the one estimated using the nonupdated structural parameters

Structural Reliability Estimation with Vibration-Based Identified Parameters

This paper presents a unique structural reliability estimation method incorporating structural parameter identification results based on the seismic response measurement. In the shaking table test, a three-bent concrete bridge model was shaken to different damage levels by a sequence of earthquake motions with increasing intensities. Structural parameters, stiffness and damping values of the bridge were identified under damaging seismic events based on the seismic response measurement. A methodology was developed to understand the importance of structural parameter identification in the reliability estimation. Along this line, a set of structural parameters were generated based on the Monte Carlo simulation. Each of them was assigned to the base bridge model. Then, every bridge model was analyzed using nonlinear time history analyses to obtain damage level at the specific locations. Last, reliability estimation was performed for bridges modeled with two sets of structural parameters. The first one was obtained by the nonlinear time history analysis with the Monte Carlo simulated parameters which is called nonupdated structural parameters. The second one was obtained by updating the first set in Bayesian sense based on the vibration-based identification results which is called updated structural parameters. In the scope of this paper, it was shown that residual reliability of the system estimated using the updated structural parameters is lower than the one estimated using the nonupdated structural parameters

Use of Supervisory Control and Data Acquisition for Damage Location of Water Delivery Systems

Urban water delivery systems can be damaged by earthquakes or severely cold weather. In either case, the damage cannot easily be detected and located, especially immediately after the event. In recent years, real-time damage estimation and diagnosis of buried pipelines attracted much attention of researchers focusing on establishing the relationship between damage ratio (breaks per unit length of pipe) and ground motion, taking the soil condition into consideration. Due to the uncertainty and complexity of the parameters that affect the pipe damage mechanism, it is not easy to estimate the degree of physical damage only with a few numbers of parameters. As an alternative, this paper develops a methodology to detect and locate the damage in a water delivery system by monitoring water pressure on-line at some selected positions in the water delivery systems. For the purpose of on-line monitoring, emerging supervisory control and data acquisition technology can be well used. A neural network-based inverse analysis method is constructed for detecting the extent and location of damage based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water delivery system with one location of damage, and validated by using a set of data that have never been used in the training. It is found that the method provides a quick, effective, and practical way in which the damage sustained by a water delivery system can be detected and located

Statistical Analysis of Fragility Curves

This paper presents a statistical analysis of structural fragility curves. Both empirical and analytical fragility curves are considered. The empirical fragility curves are developed utilizing bridge damage data obtained from the 1995 Hyogo-ken Nanbu (Kobe) earthquake. The analytical fragility curves are constructed on the basis of the nonlinear dynamic analysis. Two-parameter lognormal distribution functions are used to represent the fragility curves with the parameters estimated by the maximum likelihood method. This paper also presents methods of testing the goodness of fit of the fragility curves and estimating the confidence intervals of the two parameters (median and log-standard deviation) of the distribution. An analytical interpretation of randomness and uncertainty associated with the median is provided

Nonlinear Static Procedure for Fragility Curve Development

This study examines the fragility curves of a bridge by two different analytical approaches; one utilizes the time-history analysis and the other uses the capacity spectrum method. The latter approach is one of the simplified nonlinear static procedures recently developed for buildings. In this respect, a sample of 10 nominally identical but statistically different bridges and 80 ground-motion time histories are considered to account for the uncertainties related to the structural capacity and ground motion, respectively. The comparison of fragility curves by the nonlinear static procedure with those by time-history analysis indicates that the agreement is excellent for the state of at least minor damage, but not as good for the state of major damage where nonlinear effects clearly play a crucial role. Overall, however, the agreement is adequate even in the state of major damage considering the large number of typical assumptions under which the analyses of fragility characteristics are performed

Nonlinear Static Procedure for Fragility Curve Development

This study examines the fragility curves of a bridge by two different analytical approaches; one utilizes the time-history analysis and the other uses the capacity spectrum method. The latter approach is one of the simplified nonlinear static procedures recently developed for buildings. In this respect, a sample of 10 nominally identical but statistically different bridges and 80 ground-motion time histories are considered to account for the uncertainties related to the structural capacity and ground motion, respectively. The comparison of fragility curves by the nonlinear static procedure with those by time-history analysis indicates that the agreement is excellent for the state of at least minor damage, but not as good for the state of major damage where nonlinear effects clearly play a crucial role. Overall, however, the agreement is adequate even in the state of major damage considering the large number of typical assumptions under which the analyses of fragility characteristics are performed

Smart wireless sensor system for lifeline health monitoring under a disaster event

ABSTRACT This paper discusses issues of using wireless sensor systems to monitor structures and pipelines in the case of disastrous events. The platforms are deployed and monitored remotely on lifetime systems, such as underground water pipelines. Although similar systems have been proposed for monitoring seismic events and the structure health of bridges and buildings, several fundamental differences necessitate adaptation or redesign of the module. Specifically, rupture detection in water delivery networks must respond to higher frequency and wider bandwidth than those used in the monitoring of seismic events, structures, or bridges. The monitoring and detection algorithms can also impose a wide range of requirements on the fidelity of the acquired data and the flexibility of wireless communication technologies. We employ a non-invasive methodology based on MEMS accelerometers to identify the damage location and to estimate the extent of the damage. The key issues are low-noise power supply, noise floor of sensors, higher sampling rate, and the relationship among displacement, frequency, and acceleration. Based on the mentioned methodology, PipeTECT, a smart wireless sensor platform was developed. The platform was validated on a bench-scale uniaxial shake table, a small-scale water pipe network, and portions of several regional water supply networks. The laboratory evaluation and the results obtained from a preliminary field deployment show that such key factors in the implementation are crucial to ensure high fidelity of the acquired data. This is expected to be helpful in the understanding of lifeline infrastructure behavior under disastrous events

A Stochastic Multi-scale Approach for Numerical Modeling of Complex Materials - Application to Uniaxial Cyclic Response of Concrete

In complex materials, numerous intertwined phenomena underlie the overall response at macroscale. These phenomena can pertain to different engineering fields (mechanical , chemical, electrical), occur at different scales, can appear as uncertain, and are nonlinear. Interacting with complex materials thus calls for developing nonlinear computational approaches where multi-scale techniques that grasp key phenomena at the relevant scale need to be mingled with stochastic methods accounting for uncertainties. In this chapter, we develop such a computational approach for modeling the mechanical response of a representative volume of concrete in uniaxial cyclic loading. A mesoscale is defined such that it represents an equivalent heterogeneous medium: nonlinear local response is modeled in the framework of Thermodynamics with Internal Variables; spatial variability of the local response is represented by correlated random vector fields generated with the Spectral Representation Method. Macroscale response is recovered through standard ho-mogenization procedure from Micromechanics and shows salient features of the uniaxial cyclic response of concrete that are not explicitly modeled at mesoscale.Comment: Computational Methods for Solids and Fluids, 41, Springer International Publishing, pp.123-160, 2016, Computational Methods in Applied Sciences, 978-3-319-27994-