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.

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 an informative yet computationally affordable probabilistic model, which detects damage through inverse updating. Performance of structural components associated with parameters of the developed model was quantified with a damage metric. Particular emphasis is given to environmental and operational effects, parametric uncertainty and model discrepancy. Additional difficulties due to usage of costly physics-based models and noisy observations are also taken into account. The framework has been used to identify a reduction of a simulated cantilever beam elastic modulus, and anomalous features in main/stay cables and bearings of the Tamar bridge. In the latter case study, displacements, natural frequencies, temperature and traffic monitored throughout one year were used to form a reference baseline, which was compared against a current state, based on one month worth of data. Results suggest that the proposed approach can identify damage with a small error margin, even under the presence of model discrepancy. However, if parameters are sensitive to environmental/operational effects, as observed for the Tamar bridge stay cables, false alarms might occur. Validation with monitored data is also highlighted and supports the feasibility of the proposed approach.

Wall crack detection is one of the primary tasks in determining the structural integrity of a building for both restorative and preventive attempts. Machine learning techniques, such as deep learning (DL) with computer vision capabilities, have gradually become more prevalent as they can provide expert assessments with an acceptable performance when the crack detection involves a considerable number of structures. Despite such a prospective application, classification on different types of wall cracks is relatively less common, possibly due to the absence of the professional-standard-to-dataset translation. In this work, we utilised a complete pipeline, starting from novel dataset construction, ground truth formulation based on civil engineering standards, and training and testing steps. Our work focused on multi-class classification with regard to the binary classification (i.e., determining only two categories) used in previous studies. We implemented transfer learning based on VGG16 and RestNET50 for feature extraction, combined them with an ANN and kNN for the classifier, and compared their prediction performances. Our results indicate that the developed models can distinguish images that contain wall cracks into three categories of features based on the degree of damage: light, medium, and severe. Furthermore, since greyscale images offer more precise readings and predictions, the use of augmentation in dataset generation is critical. Although ResNet50 is the most stable network in terms of accuracy, it performs better when paired with kNN.

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, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.Design/methodology/approach: The CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.Findings: The CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.Originality/value: This work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

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.

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.