Using deep learning (DL) to recognize building and infrastructure damage via images is becoming popular in vision-based structural health monitoring (SHM). However, many previous studies solely work on the existence of damage in the images and directly treat the problem as a single-attribute classification or separately focus on finding the location or area of the damage as a localization or segmentation problem. Abundant information in the images from multiple sources and intertask relationships are not fully exploited. In this study, the vision-based SHM problem is first reformulated into a multiattribute multitask setting, where each image contains multiple labels to describe its characteristics. Subsequently, a general multiattribute multitask detection framework, namely ϕ-NeXt, is proposed, which introduces 10 benchmark tasks including classification, localization, and segmentation tasks. Accordingly, a large-scale data set containing 37,000 pairs of multilabeled images is established. To pursue better performance in all tasks, a novel hierarchical framework, namely multiattribute multitask transformer (MAMT2) is proposed, which integrates multitask transfer learning mechanisms and adopts a transformer-based network as the backbone. Finally, for benchmarking purposes, extensive experiments are conducted on all tasks and the performance of the proposed MAMT2 is compared with several classical DL models. The results demonstrate the superiority of the MAMT2 in all tasks, which reveals a great potential for practical applications and future studies in both structural engineering and computer vision. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13067

Multiattribute multitask transformer framework for vision‐based structural health monitoring

: With the development of artificial intelligence technology, the basic judgment of students learning state can be realized through the comprehensive analysis of students face, expression, behavior posture and other multi-modal data.In this paper, we present a new image recognition algorithm based on improved AlexNet and shared parameter transfer learning. The main idea of the proposed method is to apply the technique of shared parameter transfer learning (SPTL) to improve the performance of deep neural network architectures such as AlexNet. Compared with conventional methods, SPTL has several advantages: 1) it can be applied for non-convex optimization problems; 2) it can provide better training speed and robustness; 3) it can achieve high accuracy in both training and testing stages. In addition, our method is easy to implement which it does not require any special prepossessing. After the completely associated layer comes to 10, the acknowledgment rate can be near to 98%.

/pdf/image-recognition-algorithm-based-on-improved-alexnet-and-tvwtbzgx.pdf

Image Recognition Algorithm Based on Improved AlexNet and Shared Parameter Transfer Learning

Abstract Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process (GP), trust region, and Thompson sampling are employed in the TuRBO and contribute considerably to performance in terms of computational speed, solution quality, and scalability. Empirical studies are carried out using data from Mudanjiang and Chengdu, China. The performance of TuRBO is compared with that of Bayesian optimization (BO), genetic algorithm and random sampling. The results show that TuRBO converges the fastest because of its ability to balance exploration and exploitation through the trust region and Thompson sampling. Meanwhile, because TuRBO enables more efficient exploitation through the local GP, the solution quality of TuRBO outperforms others significantly. The average waiting time achieved by TuRBO was 2.84% lower than that achieved by BO. Finally, the method has been successfully extended to a large network with 233‐dimensional spaces and 122 signalized intersections, demonstrating that the developed methodology can deal with high‐dimensional traffic signal control effectively for real case applications. 

Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization

Quantifying the Value of Information Transfer in Population-Based SHM

This paper proposes a multitask learning framework for probabilistic model updating by jointly using strain and acceleration measurements. This framework can enhance the structural damage assessment and response prediction of existing steel frame structures with quantified uncertainty. Multitask learning may be used to address multiple similar inference tasks simultaneously to achieve a more robust prediction performance by transferring useful knowledge from one task to another, even in situations of data scarcity. In the proposed model-updating procedure, a spatial frame is decomposed into multiple planar frames that are viewed as multiple tasks and jointly analyzed based on the hierarchical Bayesian model, leading to robust estimation results. The procedure uses a displacement-stress relationship in the modal space because it directly reflects the elemental stiffness and requires no prior knowledge concerning the mass, unlike most existing model-updating techniques. Validation of the proposed framework by using a full-scale vibration test on a one-story, one-bay by one-bay moment resisting steel frame, wherein structural damage to the column bases is simulated by loosening the anchor bolts, is presented. The experimental results suggest that the displacement-stress relationship has sufficient sensitivity toward localized damage, and the Bayesian multitask learning approach may result in the efficient use of measurements such that the uncertainty involved in model parameter estimation is reduced. The proposed framework facilitates more robust and informative model updating. 

Probabilistic model updating of steel frame structures using strain and acceleration measurements: A multitask learning framework

A population‐level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilizing an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different subgroups, representing (1) use‐type, (2) component, or (3) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet (15% and 13% increases in predictive log‐likelihood of hazard) and power prediction in a wind farm (up to 82% reduction in the standard deviation of maximum output prediction). In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when subfleets are allowed to share correlated information at different levels in the hierarchy; the (averaged) reduction in standard deviation for interpretable parameters in the survival analysis is 70%, alongside 32% in wind farm power models. In turn, groups with incomplete data automatically borrow statistical strength from those that are data‐rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e., parameter). Successes in both case studies demonstrate the wide applicability in practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.

/pdf/hierarchical-bayesian-modeling-for-knowledge-transfer-across-15j69ak9.pdf

Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising factor, such as in Bayesian analysis. We show theoretically and empirically that the KSD test can suffer from low power when the target and the alternative distributions have the same well-separated modes but differ in mixing proportions. We propose to perturb the observed sample via Markov transition kernels, with respect to which the target distribution is invariant. This allows us to then employ the KSD test on the perturbed sample. We provide numerical evidence that with suitably chosen transition kernels the proposed approach can lead to substantially higher power than the KSD test.

Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy

Bayesian Optimisation (BO) methods seek to ﬁnd global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective function, quantifying the uncertainty in that surrogate through Bayesian inference. Objective evaluations are sequentially determined by maximising an acquisition function at each step. However, this ancilliary optimisation problem can be highly non-trivial to solve, due to the non-convexity of the acquisition function, particularly in the case of batch Bayesian optimisation, where multiple points are selected in every step. In this work we reformulate batch BO as an optimisation problem over the space of probability measures. We construct a new acquisition function based on multipoint expected improvement which is convex over the space of probability measures. Practical schemes for solving this ‘inner’ optimisation problem arise naturally as gradient ﬂows of this objective function. We demonstrate the efﬁcacy of this new method on different benchmark functions and compare with state-of-the-art batch BO methods.

Batch Bayesian Optimization via Particle Gradient Flows

Data from populations of systems are prevalent in many industrial applications. Machines and infrastructure are increasingly instrumented with sensing systems, emitting streams of telemetry data with complex interdependencies. In practice, data-centric monitoring procedures tend to consider these assets (and respective models) as distinct -- operating in isolation and associated with independent data. In contrast, this work captures the statistical correlations and interdependencies between models of a group of systems. Utilising a Bayesian multilevel approach, the value of data can be extended, since the population can be considered as a whole, rather than constituent parts. Most interestingly, domain expertise and knowledge of the underlying physics can be encoded in the model at the system, subgroup, or population level. We present an example of acoustic emission (time-of-arrival) mapping for source location, to illustrate how multilevel models naturally lend themselves to representing aggregate systems in engineering. In particular, we focus on constraining the combined models with domain knowledge to enhance transfer learning and enable further insights at the population level.

pdf/encoding-domain-expertise-into-multilevel-models-for-source-1g7t6q4e.pdf

Encoding Domain Expertise into Multilevel Models for Source Location

We propose a population-level analysis to address issues of data sparsity when building predictive models of engineering infrastructure. By sharing information between similar assets, hierarchical Bayesian modelling is used to improve the survival analysis of a truck ﬂeet (hazard curves) and power prediction in a wind farm (power curves). In each example, a set of correlated functions are learnt over the asset ﬂeet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-ﬂeets of assets are allowed to share correlated information at diﬀerent levels in the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The correlations can be inspected to inform which assets share information for which eﬀect (i.e. parameter).

Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning

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