This paper presents a feature extraction method to uncover the temperature effects on bridge responses, which combines mode decomposition, data reduction, and blind separation. For empirical decomposition (EMD) ensemble (EEMD) have been executed, followed by principal component analysis (PCA) for size compression. Independent (ICA) is then employed The unique of proposed separation, enables temperature-induced response be extracted from mixed structural responses without any prior...

Despite the recent advances in sensor technologies and data-acquisition systems, interpreting measurement data for structural monitoring remains a challenge. Furthermore, because of complexity structures, materials used, uncertain environments, behavioral models are difficult to build accurately. This paper presents novel model-free data-interpretation methodologies that combine moving principal component analysis (MPCA) with each four regression-analysis methods—robust regression (RRA),...

This article presents a probabilistic structural identification of the Tamar bridge using detailed finite element model. Parameters cables initial strain and bearings friction were identified. Effects temperature traffic jointly considered as driving excitation bridge’s displacement natural frequency response. Structural is performed with modular Bayesian framework, which uses multiple response Gaussian processes to emulate model surface its inadequacy, that is, discrepancy. In addition,...

Abstract Accurate structural behavior interpretation via finite element models is often disrupted by uncertainties, while data-driven approaches can struggle with long datasets, complex fluctuations, and the omission of essential spatio-temporal features. Additionally, these methods are limited their reliance on interpolative predictions. This paper introduces a novel, model-free approach that integrates Moving Principal Component Analysis (MPCA), bidirectional gated recurrent units (biGRU),...

The measurement system configuration is an important task in structural health monitoring that decisions influence the performance of systems. This generally performed using only engineering judgment and experience. Such approach may result either a large amount redundant data high data-interpretation costs, or insufficient leading to ambiguous interpretations. paper presents systematic configure systems where static are interpreted for damage detection model-free (non–physics-based)...

We address the problem of damage identification in complex civil infrastructure with an integrative modular Bayesian framework. The proposed approach uses multiple response Gaussian processes to build informative yet computationally affordable probabilistic model, which detects through inverse updating. Performance structural components associated parameters developed model was quantified a metric. Particular emphasis is given environmental and operational effects, parametric uncertainty...

Wall crack detection is one of the primary tasks in determining structural integrity a building for both restorative and preventive attempts. Machine learning techniques, such as deep (DL) with computer vision capabilities, have gradually become more prevalent they can provide expert assessments an acceptable performance when involves considerable number structures. Despite prospective application, classification on different types wall cracks relatively less common, possibly due to absence...

Many post-disaster and post-conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility reconstruction. In particular, as the number of ageing deteriorating bridges increases, it is necessary to quantify load characteristics in order inform maintenance asset databases. The carrying capacity design are considered main aspects any civil structures. Human examination can be costly slow when expertise lacking challenging scenarios. this...

Purpose Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, effectiveness of SHM system might be hindered by unprecedented factors that impact continuity data collection. This research presents a framework utilising convolutional neural network (CNN) estimating structural response using environmental variations....

Despite extensive study, performing Rapid visual screening is still a challenging task for many countries. The challenges include the lack of trained engineers, limited resources, and large building inventory to detect. One most important aspect in rapid establish classification based on guidelines' specific criteria. This study proposes general framework Convolutional Neural Network perform automated procedure. method classifies buildings Federal Emergency Management Agency (FEMA)-154...

A method is proposed for the identification of instantaneous frequencies (IFs) in time-varying structures. The combines a maximum gradient algorithm and smoothing operation. designed to extract wavelet ridges response signals. operation, based on polynomial curve fitting threshold method, employed reduce effects random noises. To verify effectiveness accuracy numerical example signal with two frequency modulated components investigated an experimental test steel cable tensions also...

Abstract Currently, there is a limited number of tools that can be used to assess progressive damage buildings in large-scale study areas. The effectiveness such also constrained by lack sufficient and reliable data from the area itself. This research article presents an innovative framework for detection classification precast concrete (PC) based on satellite infrared (IR) imagery. uses heat leakage changes over time buildings. Multispectral images are spatial scanning assessment area. A...

Keywords: Structural Health Monitoring (SHM) ; damage detection measurement system configuration detectability time to multi-objective optimization Multi Criteria Decision Making (MCDM) Moving Principal Component Analysis (MPCA) regression analysis These Ecole polytechnique federale de Lausanne EPFL, n° 5518 (2013)Programme doctoral StructuresFaculte l'environnement naturel, architectural et construitInstitut d'ingenierie civileLaboratoire d'informatique mecanique appliquees a la...

Interpreting measurement data from continuous monitoring of civil structures for structural health (SHM) is a challenging task. This task even more difficult when are influenced by environmental variations, such as temperature, wind and humidity. paper investigates the first time performance two model-free interpretation methods: Moving Principal Component Analysis (MPCA) Robust Regression (RRA) that temperature. The methods evaluated through criteria: (1) damage detectability (2) to...

In this paper, blind source separation idea is employed to identify abnormal variations in time-history sensor measurements, which can be the indication of structural damage inside bridge. Since measurements are mixed responses due complicated surroundings, environmental impact should not ignored any more. The reason only change properties but also mask response changes that indicate performance degradation. As it difficult procure complex loading proposed method, thus, employs principal...