Structural performance assessment considering both observed and latent environmental and operational conditions: A Gaussian process and probability principal component analysis method

Structural faults like damage and degradations will cause changes in structure response data. Performance assessment can be conducted by investigating such changes. In real implementations however, structural responses are affected by environmental and operational variations (EOVs) as well. Such variation should be well captured by the assessment model when detecting structural changes. It should be noted that not all EOVs can be measured by the monitoring system. When both observed and latent EOVs have significant effects on the monitored structural responses, these two effects should be considered properly. Furthermore, uncertainties can be significant for the monitoring data since loads and EOVs cannot be directly controlled under working conditions. To address these problems, this work proposes a performance assessment method considering both observed and latent EOVs. A Gaussian process is used to model the functional behaviour between structural response and observed EOVs whilst principal component analysis is used to eliminate the effect of latent EOVs. These two methods are combined using a Bayesian formulation and the effect of both observed and latent EOVs are modelled. The associated model parameters are inferred through probability density functions to account for the uncertainties. A synthetic data example is presented to validate the proposed method. It is also applied to the monitoring data of a long-span cable-stayed bridge with different damage scenarios considered, illustrating its capability of real implementations in structural health monitoring.

Experimental Study on the Behavior of Steel–Concrete Composite Decks with Different Shear Span-to-Depth Ratios

This paper presents the results of an experimental study on the mechanical behaviors of steel‒concrete composite decks with different shear span-to-depth ratios. Herein, four composite decks categorized into two types with shear span-to-depth ratios of 2.5 and 4.6 are designed for an experimental program. The decks then undergo the four-point bending tests until failure to investigate the structural responses, such as the load, displacement, crack mechanism, and failure mode. Conventional section analysis is used to derive the flexural strength of composite decks in comparison with the test results. Additionally, the ductility of the composite decks is assessed based on the displacement indices. The analysis results demonstrate that the stiffness and capacity of the composite deck increase with the decrease in the shear span length. However, the ductility of the composite slabs increases with the shear span length. The flexural strengths predicted by section analysis overestimate the actual test results. The shear span-to-depth ratio affects the crack mechanism of the composite decks.

Verification of a Stiffness-Variable Control System with Feed-Forward Predictive Earthquake Energy Analysis

Semi-active isolation systems with controllable stiffness have been widely developed in the field of seismic mitigation. Most systems with controllable stiffness perform more robustly and effectively for far-field earthquakes than for near-fault earthquakes. Consequently, a comprehensive system that provides comparable reductions in seismic responses to both near-fault and far-field excitations is required. In this regard, a new algorithm called Feed-Forward Predictive Earthquake Energy Analysis (FPEEA) is proposed to identify the ground motion characteristics of and reduce the structural responses to earthquakes. The energy distribution of the seismic velocity spectrum is considered, and the balance between the kinetic energy and potential energy is optimized to reduce the seismic energy. To demonstrate the performance of the FPEEA algorithm, a two-degree-of-freedom structure was used as the benchmark in the numerical simulation. The peak structural responses under two near-fault and far-field earthquakes of different earthquake intensities were simulated. The isolation layer displacement was suppressed most by the FPEEA, which outperformed the other three control methods. Moreover, superior control on superstructure acceleration was also supported by the FPEEA. Experimental verification was then conducted with shaking table test, and the satisfactory performance of the FPEEA on both isolation layer displacement and superstructure acceleration was demonstrated again. In summary, the proposed FPEEA has potential for practical application to unexpected near-fault and far-field earthquakes.

Structural sensitivity of an asphalt pavement to asphalt binder content and mix design method

The goal of this study was to analyze the structural sensitivity of a flexible pavement, whose asphalt layers underwent variations in its mechanical properties due to the asphalt binder content and the mix design method Marshall and Superpave. A variation of ±0.5% within the optimum asphalt binder contents was used (service tolerance) considering possible permissible variations in the asphalt binder content during the asphalt mixture manufacturing process. The values of resilient modulus and indirect tensile strength (Brazilian test) of the resulting asphalt mixtures were applied to the reference pavement structure analyzed by the me-PADS software. The results show that the variations in the asphalt binder content and the asphalt mixtures design method influence the mechanical properties and corresponding structural responses of the pavement investigated: the asphalt layers designed by the Marshall method presented greater sensitivity to the variation in asphalt binder content, which may constitute a technical differential of asphalt mixtures designed by the Superpave method.

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses. Parallel to that, an existing dataset of a similar vessel (Hull 061) was analysed using unsupervised and supervised learning models; these were found to be beneficial for the classification of bow entry events according to key kinematic parameters. A comparison of different algorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition, using empirical probability distributions, the likelihood of wet-deck slamming was estimated given a vertical bow acceleration threshold of 1 in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.