Kristof Maes

Identification and Modelling of Vertical Human-Structure Interaction

Slender footbridges are often highly susceptible to human-induced vibrations, due to their low stiffness, damping and modal mass. Predicting the dynamic response of these civil engineering structures under crowd-induced loading has therefore become an important aspect of the structural design. The excitation of groups of pedestrians and crowds is generally modelled using moving loads but also the changes in dynamic characteristics due to human-structure interaction are found to significantly affect the footbridge response. The present contribution investigates the influence of the presence of the pedestrians onto the dynamic characteristics of the occupied structure by means of an extensive experimental study on a footbridge in laboratory conditions. The analysis shows that the natural frequencies slightly reduce due to the additional mass but more significant is the observed increase in structural damping. Similar observations are made on a in situ footbridge. This interaction is simulated using a coupled human-structure model in which the human occupants are represented by simple biomechanical models.

The impact of vertical human-structure interaction for footbridges

Abstract. To allow for a more accurate description of crowd-induced loading, the present study aims to provide the necessary insights into vertical human-structure interaction (HSI) phenomena, to date ignored in current load models. A parametric study is performed to investigate the effects of the mechanical interaction between pedestrians, represented by simple lumped parameter models, and the supporting footbridge. The most significant HSI-effect for the lowfrequency modes of a footbridge, is in the effective damping ratio of the coupled system which is much higher than the inherent structural damping. The effective damping ratio increases monotonically with the pedestrian density but is highly dependent on the natural frequency of the footbridge. In order to verify the findings, a comprehensive full-scale experimental study is performed on two footbridges. It is shown that the crowd-structure model is able to reproduce the experimentally identified dynamic characteristics of the coupled system.

Continuous strain prediction for fatigue assessment of an offshore wind turbine using Kalman filtering techniques

Offshore wind turbines are exposed to continuous wind and wave excitation. The continuous monitoring of high periodic strains at critical locations is important to assess the remaining lifetime of the structure. Some of the critical locations are not accessible for direct strain measurements, e.g. at the mud-line, 30 meter below the water level. Response estimation techniques can then be used to estimate the response at unmeasured locations from a limited set of response measurements and a system model. This paper shows the application of a Kalman filtering algorithm for the estimation of strains in the tower of an offshore monopile wind turbine in the Belgian North Sea. The algorithm makes use of a model of the structure and a limited number of response measurements for the prediction of the strain responses. It is shown that the Kalman filter algorithm is able to account for the different types of excitation acting on the structure in operational conditions, in this way yielding accurate strain estimates that can be used for continuous fatigue assessment of the wind turbine.

Online Input and State Estimation in Structural Dynamics

This paper presents two applications of joint input-state estimation in structural dynamics. The considered joint input-state estimation algorithm relies upon a limited set of response measurements and a system model, and can be applied for online input and state estimation on structures. In the first case, the algorithm is applied for force identification on a footbridge. The second case shows an application where strains in the tower of an offshore monopile wind turbine are estimated. In both cases, real measured data obtained from in situ measurements are used for the estimation. The dynamic system model, used in the estimation, is for both case studies obtained from a finite element model of the structure. The quality of the force and response estimates is assessed by comparison with the corresponding measured quantities.

Uncertainty quantification for joint input-state estimation in structural dynamics

This paper presents a novel approach for quantification of the estimation uncertainty on the results obtained from joint input-state estimation in structural dynamics. The uncertainty accounted for originates from measurement errors and unknown stochastic excitation, that is acting on the structure besides the forces that are to be identified. The uncertainty quantification approach is applied for a joint input-state estimation algorithm that is used for force identification and response estimation in structural dynamics. The approach can, however, be extended to other force and state estimation algorithms. The uncertainty on the estimated quantities can be used to design a sensor network and to determine the optimal noise statistics that are applied for joint input-state estimation. The uncertainty quantification approach is verified using numerical simulations.

Uncertainty Quantification for Force Identification and Response Estimation in Structural Dynamics

Three sources of uncertainty are present when applying system inversion techniques for force identification and response estimation in structural dynamics; measurement noise, modeling errors, and unmodeled excitation that is acting on the structure besides the forces that are identified. The latter may for example consist of wind loads or other sources of ambient excitation. This paper presents a novel approach for quantification of the estimation uncertainty introduced by measurement noise and unmodeled excitation. The proposed methodology is applied for a state-of-the-art joint input-state estimation algorithm, but can be easily extended to other force identification and response estimation algorithms. The uncertainty on the estimated quantities can be used to design a sensor network and to determine the optimal noise statistics that are applied for joint input-state estimation. A validation is performed using data obtained from full-scale experiments on a footbridge.

Identification of Human-Induced Loading Using a Joint Input-State Estimation Algorithm

This paper uses a state-of-the-art joint input-state estimation algorithm to identify the modal load induced by a single pedestrian on a laboratory structure. The experimental setup involves a simply-supported concrete slab with a length of 7 m. A dynamic model of the lab structure is constructed from a finite element model that is calibrated using a set of experimentally identified modal characteristics. The estimated modal load as is compared with the numerically predicted modal load which uses the average single-step walking load as determined from direct force measurements, the location of the individual steps as identified from video processing and a numerical model of the structure. For the time interval where the pedestrian is crossing the slab, the estimated modal load is found to be in good agreement with the numerically predicted values. Following the last footstep of the pedestrian, the slab passes into a decaying free vibration whereby an exponentially decaying estimated input compensates for small errors in the modal properties.

A Comparison of Two Data Acquisition Techniques for Modal Strain Identification from Sub-microstrain FBG Data

The identification of changes in the dynamic characteristics which are related to structural damage is the core principle of the vibration-based structural health monitoring (VBSHM). By addressing these changes, engineers can acquire valuable information about the condition of structures. Eigenfrequencies are influenced by environmental factors and this influence can be high enough to completely mask the effect of damage while they are also global structural characteristics and they cannot asses the location of the damage. A promising alternative is the monitoring of modal strains which offers the benefit of high sensitivity to local damage. However, current measurement techniques encounter difficulties in capturing the very small strain (sub-microstrain) levels that occur during ambient or operational excitation with sufficient accuracy and precision.

SYNCHRONIZATION OF DATA ACQUISITION SYSTEMS FOR THE PURPOSE OF STRUCTURAL HEALTH MONITORING

This paper presents a technique for offline time synchronization of data acquisition systems for linear structures with proportional damping. The technique can be applied when direct synchronization of data acquisition systems is impossible or not sufficiently accurate, for example when multiple measurement nodes with different clock signals are embedded in a health monitoring network. The synchronization is based on the acquired dynamic response of the structure only, and does not require the acquisition of a shared sensor signal or a trigger signal. The time delay is identified from the spurious phase shift of the mode shape components that are obtained from system identification. A demonstration for a laboratory experiment on a cantilever steel beam shows that the proposed methodology can be used for accurate time synchronization, resulting in a significant improvement of the accuracy of the identified mode shapes. 2719 Available online at www.eccomasproceedia.org Eccomas Proceedia COMPDYN (2017) 2719-2728

Verification of joint input-state estimation by means of a full scale experiment on a footbridge

This paper presents a verification of a state-of-the-art joint input-state estimation algorithm using data obtained from in situ experiments on a footbridge. A dynamic model of the footbridge is based on a detailed finite element model that is calibrated using a set of experimental modal characteristics. The joint input-state estimation algorithm is used for the identification of two impact, harmonic, and swept sine forces applied to the bridge deck. In addition to these forces, unknown stochastic forces, such as wind loads, are acting on the structure. These forces, as well as measurement errors, give rise to uncertainty in the estimated forces and system states. Quantification of the uncertainty requires determination of the power spectral density of the unknown stochastic excitation, which is identified from the structural response under ambient loading. The verification involves comparing the estimated forces with the actual, measured forces. Although a good overall agreement is obtained between the estimated and measured forces, modeling errors prohibit a proper distinction between multiple forces applied to the structure for the case of harmonic and swept sine excitation.