Structural identification through continuous monitoring: data cleansing using temperature variations

The aim of structural performance monitoring is to infer the state of a structure from measurements and thereby support decisions related to structural management. Complex structures may be equipped with hundreds of sensors that measure quantities such as temperature, acceleration and strain. However, meaningful interpretation of data collected from continuous monitoring remains a challenge. MPCA (Moving principal component analysis) is a model-free data interpretation method which compares characteristics of a moving window of measurements against those derived from a reference period. This paper explores a data cleansing approach to improve the performance of MPCA. The approach uses a smoothing procedure or a low-pass filter (moving average) to exclude the effects of seasonal temperature variations. Consequently MPCA can use a smaller moving window and therefore detect anomalies more rapidly. Measurements from a numerical model and a prestressed beam are used to illustrate the approach. Results show that removal of seasonal temperature effects can improve the performance of MPCA. However, improvement may not be significant and there remains a trade off when choosing the window size. A small window increases the risk of false-positives while a large window increases the time to detect damage

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

© The Author(s) 2018. This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods

Automated building classification framework using convolutional neural network

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 a large building inventory to detect. One of the most important aspect in rapid visual screening is to establish the building classification based on the guidelines’ specific criteria. This study proposes a general framework based on Convolutional Neural Network to perform automated building classification for the rapid visual screening procedure. The method classifies buildings based on the Federal Emergency Management Agency (FEMA)-154 guidelines and uses transfer learning techniques from a pre-trained network. The Indonesian building portfolio is used as a case study and a dataset of building images generated through web-scraping on Google Search™ engines and Google StreetView™ website is used for the method validation. Results show that the proposed framework has promising potential to automate the building classification based on FEMA-154 guidelines

Combined Model-Free Data-Interpretation Methodologies for Damage Detection during Continuous Monitoring of Structures

Despite the recent advances in sensor technologies and data-acquisition systems, interpreting measurement data for structural monitoring remains a challenge. Furthermore, because of the complexity of the structures, materials used, and 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 of four regression-analysis methodsrobust regression analysis (RRA), multiple linear analysis (MLR), support vector regression (SVR), and random forest (RF)for damage detection during continuous monitoring of structures. The principal goal is to exploit the advantages of both MPCA and regression-analysis methods. The applicability of these combined methods is evaluated and compared with individual applications of MPCA, RRA, MLR, SVR, and RF through four case studies. Result showed that the combined methods outperformed noncombined methods in terms of damage detectability and time to detection. (C) 2013 American Society of Civil Engineers

An innovative structural health assessment tool for existing precast concrete buildings using deep learning methods and thermal infrared satellite imagery

Currently, there is a limited number of tools that can be used to assess progressive damage of buildings in large-scale study areas. The effectiveness of such tools is also constrained by a lack of sufficient and reliable data from the buildings and the area itself. This research article presents an innovative framework for damage detection and classification of precast concrete (PC) buildings based on satellite infrared (IR) imagery. The framework uses heat leakage changes over time to assess the progressive damage of buildings. Multispectral satellite images are used for a spatial scanning and large-scale assessment of a study area. A deep learning object detection algorithm coupled with two pixel intensities classification approaches are utilized in the framework. The proposed framework is demonstrated on two case study areas (parts of Karaganda and Almaty cities) in Kazakhstan using a set of multitemporal satellite images. Overall, the proposed framework, in combination with a YOLOv3 algorithm, successfully detects 85% of the PC buildings in the study areas. The use of a peak heat leakage classification approach (in comparison to mean heat leakage classification) over the 4 years showed a good agreement with the proposed framework. On-site visual inspections confirmed that PC buildings that were classified as having “High damage probability” have indeed evident signs of deterioration, as well as a more heat leakage than the rest of the buildings in the study areas. Whilst the framework has some limitations such as its applicability to extreme continental climate and its low sensitivity to detect minor damage, the proposed innovative framework showed very promising results at detecting progressive damage in PC buildings. This article contributes towards developing more efficient long-term damage assessment tools for existing buildings in large urban areas

Measurement System Configuration for Damage Identification of Continuously Monitored Structures

Measurement system configuration is an important task in structural health monitoring in that decisions influence the performance of monitoring systems. This task is generally performed using only engineering judgment and experience. Such approach may result in either a large amount of redundant data and high data‐interpretation costs, or insufficient data leading to ambiguous interpretations. This paper presents a systematic approach to configure measurement systems where static measurement data are interpreted for damage detection using model‐free (non‐physics‐based) methods. The proposed approach provides decision support for two tasks: (1) determining the appropriate number of sensors to be employed and (2) placing the sensors at the most informative locations. The first task involves evaluating the performance of measurement systems in terms of the number of sensors. Using a given number of sensors, the second task involves configuring a measurement system by identifying the most informative sensor locations. The locations are identified based on three criteria: the number of non‐detectable damage scenarios, the average time to detection and the damage detectability. A multi‐objective optimization is thus carried out leading to a set of non‐dominated solutions. To select the best compromise solution in this set, two multi criteria decision making methods, Pareto‐Edgeworth‐Grierson multi‐criteria decision making (PEG‐MCDM) and Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE), are employed. A railway truss bridge in Zangenberg (Germany) is used as a case study to illustrate the applicability of the proposed approach. Measurement systems are configured for situations where measurement data are interpreted using two model‐free methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA). Results demonstrate that the proposed approach is able to provide engineers with decision support for configuring measurement systems based on the data‐interpretation methods used for damage detection. The approach is also able to accommodate the simultaneous use of several model‐free data‐interpretation methods. It is also concluded that the number of non‐detectable scenarios, the average time to detection and the damage detectability are useful metrics for evaluating the performance of measurement systems when data are interpreted using model‐free methods

Utilization of high-volume fly ash in pervious concrete mixtures for mangrove conservation

In environmental conservation, mangrove forests play a crucial role. Retransplanting mangrove propagules, however, faces challenges, and success rates are notably low. Achieving an optimal protector for propagules, balancing strength without impeding growth, is challenging. Mangrove propagules require a temporary protector with an optimal balance, neither too weak nor too strong, to shield them from current waves which is difficult. We propose using pervious concrete pots with high-volume fly ash activated with low NaOH concentrations. The investigation focuses on the influence of the mixing procedure on workability, compressive strength, and mineral composition. The novel discovery in this study is the specific sequence of stirring the ingredients using an alkali activator, which adds an interesting dimension to the research. It is recommended to adopt Sequence 2 in pervious concrete production, where NaOH dissolved FA in the mixture forming albite as N-A-S-H gel product. It surely enhanced both workability and the strength confirming uniform application processes. The two recommended variants, PFS-60 and PFBS-50, effectively utilize coal ash, meeting the target compressive strength range of 3–5 MPa and providing support for mangrove pots over a 3–4 year period. Notably, both compositions maintained consistent mechanical properties during exposure to tidal conditions for 240 days.They exhibit high permeability (694 liter/m²/minute), facilitating efficient water passage without sediment entrainment

Model-Free Methodologies for Data-Interpretation during Continuous Monitoring of Structures

Most civil engineering infrastructures, especially bridges worldwide, are approaching the end of their designed lifespan. They are continuously deteriorating due largely to material aging, excessive loads and changing environments. Therefore, it is crucial to evaluate the performance of existing structures to prevent catastrophic events. Structural Health Monitoring (SHM) has the potential to provide a proper assessment of structural performance and to further reduce cost through early damage detection and thus replacement avoidance. SHM integrates technologies to monitor structural behaviour and with current advances in sensor technology and measurement systems, the number of bridges that are continuously monitored is increasing. The bottleneck in SHM is data interpretation and this task is even more challenging in the presence of environmental variations. The main stream of data interpretation in SHM involves physics-based modelling and validation. However, building a model can be expensive, time consuming and difficult due to the structural complexity and uncertain environments. This research focuses on model-free methodologies for continuous monitoring of civil structures under environmental variations. The work involves important aspects of SHM such as damage detection, measurement system configuration and environmental variations. For damage detection, a novel model-free methodology that combines Moving Principal Component Analysis (MPCA) and regression analyses is proposed. Such approach aims to exploit the advantages of both MPCA and regression-analysis methods. The methodology has been tested on numerical and experimental studies including a full-scale bridge application. Results of a comparative study with other model-free methodologies demonstrate the superior performance of the proposed methodology in terms of damage detectability and time to detection. This research work also compares the performance of model-free methodologies for predicting natural frequencies of a continuously monitored bridge under environmental variations. Relative importance of environmental factors and traffic loading is also evaluated. Results of the case study reveal that traffic loading and temperature variations are the most influential parameters. A structural identification strategy that takes advantage of thermal variations is developed. The strategy utilizes thermal variations as load cases to evaluate structural performance. Results exhibit the promising potential of the strategy for enhancing structural identification tasks. As for measurement system configuration, a systematic approach that involves multi-objective optimization and Multi Criteria Decision Making (MCDM) methodologies is proposed. The approach is able to accommodate model-free methodologies for damage detection and provide support for selecting the best compromise configuration. It is also applicable for situations where several model-free methods are used for data interpretation