Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L_∞-norm-based Gaussian likelihood function), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability–density functions. The application of these data-interpretation methodologies for condition assessment and fatigue life prediction is illustrated on a highway steel–concrete composite bridge having four spans with a total length of 219 m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time–stress response at a fatigue critical location along the bridge span. The time–stress response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue life compared with residual minimization and traditional Bayesian model updating in the presence of correlated non-Gaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practicing engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling.

With increasing urbanization and depleting reserves of raw materials for construction, sustainable management of existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge of infrastructure behavior and improve engineering decision making for asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) and error-domain model falsification (EDMF) have been proposed to interpret monitoring data and support asset management. Application of these methodologies requires approximations and assumptions related to model class, model complexity and uncertainty estimations, which ultimately affect the accuracy of data interpretation and subsequent decision making. This paper introduces methodology maps in order to provide guidance for appropriate use of these methodologies. The development of these maps is supported by in-house evaluations of nineteen full-scale cases since 2016 and a two-decade assessment of applications of model-based methodologies. Nineteen full-scale studies include structural identification, fatigue-life assessment, post-seismic risk assessment and geotechnical-excavation risk quantification. In some cases, much, previously unknown, reserve capacity has been quantified. RM and BMU may be useful for model-based data interpretation when uncertainty assumptions and computational constraints are satisfied. EDMF is a special implementation of BMU. It is more compatible with usual uncertainty characteristics, the nature of typically available engineering knowledge and infrastructure evaluation concepts than other methodologies. EDMF is most applicable to contexts of high magnitudes of uncertainties, including significant levels of model bias and other sources of systematic uncertainty. EDMF also provides additional practical advantages due to its ease of use and flexibility when information changes. In this paper, such observations have been leveraged to develop methodology maps. These maps guide users when selecting appropriate methodologies to interpret monitoring information through reference to uncertainty conditions and computational constraints. This improves asset-management decision making. These maps are thus expected to lead to lower maintenance costs and more sustainable infrastructure compared with current practice.

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and systematic bias using a physics-based model is a computationally expensive task. The computational cost of this task is exponentially proportional to the number of model parameters updated using monitoring data. In this paper, a novel model-class selection method is proposed to obtain computationally optimal and identifiable model classes. Unlike traditional sensitivity methods for model-class selection, in the proposed method, model responses at sensor locations are clustered to identify underlying trends in model response datasets. $K$ -means clustering is used to determine relevant clusters in the data. Cluster indices are then used as labels for classification. Support-vector machine classification using forward variable selection with sequential search is used to select model parameters that help classify trends in data. The result of the sequential search is a trade-off curve comparing classification error with number of parameters in the model class. This curve helps select a practical and near-optimal model class. The model-class selection method proposed in this paper is compared with linear regression-based sensitivity analysis using a full-scale bridge. Identification with model classes obtained using both methods for two sensor configurations suggests that the model-based clustering method helps select an identifiable and computationally efficient model class. The minimum remaining fatigue life of the bridge predicted using the updated model classes is 720 years and this represents fatigue-life extension of 10 times compared with design predictions prior to measurements. This approach provides good support for asset managers when they interpret measurement data.

Clinical Gait Analysis is essential for understanding functional ability and diagnosing neuromotor conditions but remains resource-intensive, often requiring expensive, immobile equipment. Wearable inertial measurement units (IMUs) offer a promising alternative for real-world functional assessment, yet a lack of standardisation in sensor placement presents a significant challenge. This study aimed to develop and evaluate deep learning models (specifically convolutional neural networks (CNN) and temporal convolutional networks (TCN),) by comparing F1 scores and temporal error percentile between predicted and target gait events using IMUs placed at clinical locations (head, pelvis, wrist, and feet) in young and older adults. Our findings demonstrated that TCN consistently scored higher F1 scores with lower standard error across both young and older adult groups. Specifically, the TCN with head sensor achieved the highest F1 scores of 95.9% in younger individuals and 93.8% in older participants for heel strike, highlighting the robustness of TCN models in capturing temporal dependencies for accurate gait event detection. In contrast, CNN models exhibited higher standard of error, with up to 20% differences between age groups showing age depedent influence. Temporal error analysis showed comparable performance between CNN and TCN models across most placements. Integrating gyroscope data with accelerometer signals improved F1 score, particularly for head and foot sensors. This research systematically evaluates optimal sensor placements and demonstrates the robust performance of TCN models, laying a foundation for future research. Expanded cohorts and validation in free-living environments are needed to enhance the generalisability of IMU-based gait analysis.

The management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Civil infrastructure often has hidden reserve capacity because of conservative approaches used in design and during construction. Information collected through sensor measurements has the potential to improve knowledge of structural behavior, leading to better decisions related to asset management. In this situation, the design of the monitoring system is an important task since it directly affects the quality of the information that is collected. Design of optimal measurement systems depends on the choice of behavior-model parameters to identify using monitoring data and non-parametric uncertainty sources. A model that contains a representation of these parameters as variables is called a model class. Selection of the most appropriate model class is often difficult prior to acquisition of information regarding the structural behavior, and this leads to suboptimal sensor placement. This study presents strategies to efficiently design measurement systems when multiple model classes are plausible. This methodology supports the selection of a sensor configuration that provides significant information gain for each model class using a minimum number of sensors. A full-scale bridge, The Powder Mill Bridge (USA), and an illustrative beam example are used to compare methodologies. A modification of the hierarchical algorithm for sensor placement has led to design of configurations that have fewer sensors than previously proposed strategies without compromising information gain.