Juan M. Caicedo

Damage Detection Accommodating Varying Environmental Conditions

Although many algorithms have been developed in the last two decades to detect damage in civil structures using dynamic properties, few studies have considered the challenge imposed by the variability of these properties due to changing environmental conditions. To address this concern, a statistically based analysis is proposed herein to analyze the distribution of identified structural parameters over an unknown number of external conditions and to effectively reduce their influence on the localization of damage. The proposed SHM scheme can be divided into three main steps: (1) identification of modal properties using acceleration responses of the structure to ambient loads under the influence of environmental conditions; (2) characterization of the structure as a function of the detected dynamic properties and an identification model (ID-model) representative of the system; and (3) accommodation of the influence of external conditions by means of a principal component analysis of the identified parameters. An analytical model of a four story, two-bay by two-bay building developed by the IASCM-ASCE Task Group on SHM benchmark problems is used to demonstrate the effectiveness of the proposed technique. The robustness of the technique is tested by considering modeling errors, as well as a considerable amount of sensor noise. Nine structural configurations are investigated with various temperatures and temperature gradients. The results indicate that the method is effective for detecting and locating damage.

Seismic response control using smart dampers

Presents the results of a numerical study conducted to demonstrate the capabilities of multiple magnetorheological (MR) devices for seismic control of civil engineering structures when used in conjunction with a clipped optimal control algorithm. The study employs an identified model of the integrated structural system that is shown to accurately predict the responses of the experimental setup in the Washington University Structural Control and Earthquake Engineering Lab. Four parallel-plate, shear-mode MR dampers are used to control a six-story structure. The control devices are arbitrarily located in the structure. Through simulation, the performance of the controlled system is examined. The results indicate that high performance levels can be achieved, and the responses of the semi-active system are significantly smaller than that of comparable passive systems.

Monitoring of bridges to detect changes in structural health

Discusses the development and implementation of a relatively low-cost health monitoring system via telemetry. The subject of the studies is the Hormiguero bridge spanning the Cauca River in Colombia. This system is being implemented for real-time monitoring of accelerations of the bridge using the Colombian Southwest Earthquake Observatory telemetry system. This two-span metallic bridge, located along a critical road between the cities of Puerto Tejada and Cali in the Cauca Valley, was constructed approximately 50 years ago. Experiences with this system demonstrate how effective low cost systems can be used to remotely monitor the structural integrity of deteriorating structures that are continuously subjected to high loading conditions. The paper discusses the health monitoring system and provide some results based on the data acquired from this system.

Probabilistic Prognosis of Fatigue Crack Growth Using Acoustic Emission Data

This paper presents a structural health monitoring methodology that uses acoustic emission (AE) features to predict crack growth in structural elements subjected to fatigue. This allows for the prediction of the failure of the structural element at the current load level. The methodology uses Bayesian inference to account for different sources of uncertainty such as uncertainty in the data (AE signal), unknown fracture mechanics parameters, and model inadequacy. The methodology is divided into two main components: a model updating component that uses available data to build a joint probability distribution of the different unknown fracture mechanics parameters, and a prognosis component in which this multivariable probability distribution is sampled to predict the stress intensity factor range at a future number of cycles. The application of the methodology does not require knowledge of the load amplitude nor the initial crack length. The methodology is validated using experimental data from a compact test specimen under cyclic loading.

A novel evolutionary algorithm for identifying multiple alternative solutions in model updating

This article proposes an evolutionary algorithm that is able to identify both global and local minima. This is accomplished by including two new operators to a traditional steady-state genetic algorithm. The proposed algorithm uses a single population in contrast to other evolutionary algorithms available in the literature. The algorithm is used to update a model of a structural system and provide the analyst with different plausible solutions for the updated models. Model updating techniques are used to enhance the behavior of numerical models of existing structures based on experimental data. Although the optimal updated model corresponds to the global minimum of the objective function, the model with the best physical representation of the structure could be a local minimum because of modeling errors, noise in the experimental data, errors in the extraction of system features from the experimental data and limited number sensors, among other factors. The evolutionary algorithm proposed in this article identifies global and local minima of the objective function, giving the analyst the option to choose the updated model from a set of plausible models. These models are specially designed to be as physically different as possible from each other providing the analyst with significantly different alternatives. The proposed methodology is validated with two numerical examples. The first example shows the capabilities of the technique with a mathematical function. A model updating problem using the American Society of Civil Engineering Structural Health Monitoring Benchmark structure is used for the second numerical example.

Investigating the role of feedstock properties and process conditions on products formed during the hydrothermal carbonization of organics using regression techniques

The purpose of this study is to develop regression models that describe the role of process conditions and feedstock chemical properties on carbonization product characteristics. Experimental data were collected and compiled from literature-reported carbonization studies and subsequently analyzed using two statistical approaches: multiple linear regression and regression trees. Results from these analyses indicate that both the multiple linear regression and regression tree models fit the product characteristics data well. The regression tree models provide valuable insight into parameter relationships. Relative weight analyses indicate that process conditions are more influential to the solid yields and liquid and gas-phase carbon contents, while feedstock properties are more influential on the hydrochar carbon content, energy content, and the normalized carbon content of the solid.

Reducing Epistemic Uncertainty Using a Model Updating Cognitive System

Developing numerical models of existing structural systems is challenging because of the uncertainty inherent on the development of the numerical model and the estimation of the structural parameters. This uncertainty is a combination of lack of knowledge (epistemic uncertainty) and inherent randomness on the system. This paper introduces a Model Updating Cognitive Systems (MUCogS) as a new paradigm for model updating of structural systems with incomplete data. MUCogS seeks to merge the computational power of computers with the analytical power of the analyst. In most cases, the posterior probability density function (PDF) within a Bayesian framework has one region of high probability. However, several regions of high probability can be obtained on the likelihood when data is incomplete. These areas can be considered by the analyst to enhance his/her knowledge about the structure. This paper discusses a methodology used to identify these regions of high probability without the need of calculating the complete likelihood using Modeling to Generate Alternatives (MGA).

Quantification of Fatigue Cracking in CT Specimens with Passive and Active Piezoelectric Sensing

Monitoring of fatigue cracks in steel bridges is of interest to bridge owners and agencies. Monitoring of fatigue cracks has been attempted with acoustic emission using either resonant or broadband sensors. One drawback of passive sensing is that the data is limited to that caused by growing cracks. In this work, passive emission was complemented with active sensing (piezoelectric wafer active sensors) for enhanced detection capabilities. Passive and active sensing methods were described for fatigue crack monitoring on specialized compact tension specimens. The characteristics of acoustic emission were obtained to understand the correlation of acoustic emission behavior and crack growth. Crack and noise induced signals were interpreted through Swansong II Filter and waveform-based approaches, which are appropriate for data interpretation of field tests. Upon detection of crack extension, active sensing was activated to measure the crack size. Model updating techniques were employed to minimize the difference between the numerical results and experimental data. The long term objective of this research is to develop an in-service prognostic system to monitor structural health and to assess the remaining fatigue life.

Modal Identification Using Mobile Sensors under Ambient Excitation

AbstractThe state-of-the-art modal identification for civil structures is limited to the estimation mode shapes with a low spatial resolution. Modal coordinates are identified only at the location of sensors, which are fixed at particular locations on the structure. Increasing the number of sensors in the structure is an alternative to increase the spatial resolution. Unfortunately, this increases the cost of the instrumentation and the data to be transmitted and processed. Another common alternative is to use numerical algorithms to expand the modal coordinates to nonmeasured degrees of freedom. However, mode shape expansion techniques could introduce errors in the identified modes. This paper presents the formulation, evaluation, and validation of a methodology for the ambient vibration–based modal identification using mobile sensors (MIMS). The methodology uses two sensors, one mobile and one stationary sensor, to identify spatially dense modes of vibration. The methodology is experimentally verified b...

Benchmark problem for human activity identification using floor vibrations

Proposes a Benchmark problem for vibration-based human activity monitoring.Describes a floor vibrations dataset to design algorithms for human monitoring.Benchmark is in seven cases in increasing difficulty.Standard metrics based on the available experimental data are proposed.An example to identify different human activity from floor vibrations is discussed. Monitoring and analyzing floor vibrations to determine human activity has major applications in fields such as health care and security. For example, structural vibrations could be used to determine if an elderly person living independently falls, or if a room is occupied or empty. Monitoring human activity using floor vibration promises to have advantages over other methods. For example, it does not have the privacy concerns of other methods such as vision-based techniques, or the compliance challenges of wearable sensors. The analysis of the signals becomes a classification problem determining the type of human activity. Unfortunately only a few research groups are performing research of this subject even though there is a significant number of techniques that could be applied to this field. To date, no systematic study about the challenges and advantages of using different types of algorithms for this problem has been performed. This paper proposes a benchmark problem to: (i) encourage researchers to design new algorithms for monitoring human activity using floor vibrations, (ii) provide a dataset to test new algorithms, and (iii) allow the comparison of proposed methods based on a set of standard metrics. The benchmark consists of seven different cases of increasing difficulty. Each case has a specific number of sensors, calibration signals, and type of floor excitation forces to be considered. The paper also proposes specific metrics that enable the direct comparison of different techniques. Research groups interested in monitoring human activity using floor vibrations are encouraged to use the experimental data and evaluation metrics published in this paper to develop their own methodologies. This will enable the community of researchers to easily compare and contrasts techniques and better understand what type of methods will be appropriate in different applications.