On robust regression analysis as a means of exploring environmental and operational conditions for SHM data

In the data-based approach to structural health monitoring (SHM), the absence of data from damaged structures in many cases forces a dependence on novelty detection as a means of diagnosis. Unfortunately, this means that benign variations in the operating or environmental conditions of the structure must be handled very carefully, lest they lead to false alarms. If novelty detection is implemented in terms of outlier detection, the outliers may arise in the data as the result of both benign and malign causes and it is important to understand their sources. Comparatively recent developments in the field of robust regression have the potential to provide ways of exploring and visualising SHM data as a means of shedding light on the different origins of outliers. The current paper will illustrate the use of robust regression for SHM data analysis through experimental data acquired from the Z24 and Tamar Bridges, although the methods are general and not restricted to SHM or civil infrastructure

Is it worth changing pattern recognition methods for structural health monitoring?

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data

Simplifying transformations for nonlinear systems: Part II, statistical analysis of harmonic cancellation

The first paper in this short sequence described the idea of a simplifying transformation and applied the concept to a numerical optimisation-based variant of normal form analysis. The idea of the numerical normal form transformation was simply to eliminate or reduce the contribution of a pre-defined set of harmonics in the system response. It was shown that reducing the defined harmonics could lead to amplification of other components of the response. The idea of the current paper is to conduct a Monte Carlo worst-case analysis to investigate how badly unconstrained harmonics might be amplified by the optimisation

A Novel Model to Study Adipose-Derived Stem Cell Differentiation

The use of three-dimensional (3D) culture systems (hydrogels) and adipose-derived stem cells (ADSCs) in regenerative medicine to advance early-stage investigation and modeling of the mechanisms of diseases, treatments, targets, etc. has recently increased. ADSCs, specifically, are utilized due to their innate programming during embryogenesis and in adult tissues in addition to their ability to differentiate into mesodermal, endodermal, and ectodermal cell-specific lineages. Of importance is that these advancements do not involve a model specimen (i.e. mice or rats) and simulate the numerous conflicting signals a migrating cell is exposed to in vivo such as chemokines, extracellular matrix (ECM), growth factors, and physical forces. However, our understanding of the cellular integration of these signals is lacking. We previously developed a novel self-organizing cellularized collagen hydrogel model that is adaptable, tunable, reproducible, and capable of mimicking the multitude of stimuli that cells experience. Our model formed toroids of cells around 24h, while data we present suggests initial migration as early as 3hr after seeding. Toroid formation appears to be a near universal process with the exception being the cancer cell lines we have tried (\u3c4). Interestingly, when cells are seeded inside the hydrogel, there is contraction of the gel, but no toroid is formed. We observed differences in the cell-cell and cell-ECM interactions in response to a changing microenvironment. Moreover, using rheology, collagen binding peptides, and scanning electron microscope (SEM), we found variation in the remodeling of hydrogels when comparing toroid gels to gels with cells embedded. Lastly, we sought to define the underlying signaling pathways that regulate ADSC directed migration and toroid formation by dissecting the CXCL12-CXCR4 pathway. This work will begin to establish toroid formation as a novel, 3D model for high-throughput investigation of diverse molecular mechanisms and disease progression

Model selection and parameter estimation in structural dynamics using approximate Bayesian computation

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours

A Meta-Learning Approach to Population-Based Modelling of Structures

A major problem of machine-learning approaches in structural dynamics is the frequent lack of structural data. Inspired by the recently-emerging field of population-based structural health monitoring (PBSHM), and the use of transfer learning in this novel field, the current work attempts to create models that are able to transfer knowledge within populations of structures. The approach followed here is meta-learning, which is developed with a view to creating neural network models which are able to exploit knowledge from a population of various tasks to perform well in newly-presented tasks, with minimal training and a small number of data samples from the new task. Essentially, the method attempts to perform transfer learning in an automatic manner within the population of tasks. For the purposes of population-based structural modelling, the different tasks refer to different structures. The method is applied here to a population of simulated structures with a view to predicting their responses as a function of some environmental parameters. The meta-learning approach, which is used herein is the model-agnostic meta-learning (MAML) approach; it is compared to a traditional data-driven modelling approach, that of Gaussian processes, which is a quite effective alternative when few data samples are available for a problem. It is observed that the models trained using meta-learning approaches, are able to outperform conventional machine learning methods regarding inference about structures of the population, for which only a small number of samples are available. Moreover, the models prove to learn part of the physics of the problem, making them more robust than plain machine-learning algorithms. Another advantage of the methods is that the structures do not need to be parametrised in order for the knowledge transfer to be performed

Analysis of Cellular Interactions Within a Collagen Hydrogel

Evidence has arisen over the past several years that use of a three- dimensional (3D) culture system provides a distinct advantage over two- dimensional (2D) systems when cellular interactions are examined in a more natural environment. Changes in morphology, speed, and directionality of cells tested in both planar and 3D matrices have all demonstrated that using 3D system is advantageous. The changes to the cellular migration patterns were shown to be dependent on several variables within the surrounding substrate including cellular content, physical environment, and the matrix chemical milieu. We have taken advantage of using collagen hydrogels as a 3D scaffold for culturing cells for an extended period of time which has led to intriguing discoveries. One such discovery is that independent of cell type, cells which were placed on top of the hydrogel formed a ring structure we termed a toroid. These toroids take the shape of the well in which they are cultured. These toroidal cells appear long, thin, and are reminiscent of spokes on a wheel. However, when cells were mixed into the collagen hydrogel, a gel contraction was observed, but the cells remained homogenous throughout and no toroid was formed. In our studies, stem cells, lens epithelial cells, cardiac fibroblasts, microvascular endothelial cells, and cancer cells, were used individually or in combination. Cells were placed on the top of collagen hydrogels to observe their behavior in this new multicellular environment. We observed that when the different cell types were mixed together they formed a tighter toroid than normal. We also investigated the movement of cells during the toroid formation. To that end, β1 integrin, a member of the integrin family of membrane receptors important for cellular adhesion and recognition, was overexpressed in cells using a plasmid tagged with Green Fluorescent Protein (GFP). We were successful at expressing GFP tagged β1 integrin in cells and observing them in the collagen matrix. Our observations will contribute to the understanding of toroid formation and form the foundation of future computational modeling experiments examining cellular behaviors in response to different microenvironments

On the usage of active learning for SHM

The key element of this work is to demonstrate a strategy for using pattern recognition algorithms to investigate correlations between feature variables for Structural Health Monitoring (SHM). The task will take advantage of data from a bridge. An informative chain of artificial intelligence tools will allow an active learning interaction between the unfolded shapes of the manifold of online data by characterising the physical shape between variables. In many data mining and machine learning applications, there is a significant supply of unlabelled data but an important undersupply of labelled data. Semi-supervised active learning, which combines both labelled and unlabelled data can offer serious access to useful information and may be the crucial element in successful decision making, regarding the health of structures

Robust methods for outlier detection and regression for SHM applications.

In this paper, robust statistical methods are presented for the data-based approach to structural health monitoring (SHM). The discussion initially focuses on the high level removal of the ‘masking effect’ of inclusive outliers. Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions of the structure must be handled very carefully, as it is possible that they can lead to false alarms. It is shown that recent developments in the field of robust regression can provide a means of exploring and visualising SHM data as a tool for exploring the different characteristics of outliers, and removing the effects of benign variations. The paper is not, in any sense, a survey; it is an overview and summary of recent work by the authors