A deep transfer learning network for structural condition identification with limited real-world training data

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data is almost always insufficient, because the real-time monitoring data of a structure only reflects a limited number of structural conditions, while the number of possible structural conditions is infinite. With insufficient monitoring data, the identification performance may significantly degrade. This study aims to tackle this challenge by proposing a deep transfer learning (TL) approach for structural condition identification. It effectively integrates physics-based and data-driven methods, by generating various training data based on the calibrated finite element (FE) model, pretraining a deep learning (DL) network, and transferring its embedded knowledge to the real monitoring/testing domain. Its performance is demonstrated in a challenging case, vibration-based condition identification of steel frame structures with bolted connection damage. The results show that even though the training data are from a different domain and with different types of labels, intrinsic physics can be learned through the pretraining process, and the TL results can be clearly improved, with the identification accuracy increasing from 81.8% to 89.1%. The comparative studies show that SHMnet with three convolutional layers stands out as the pretraining DL architecture, with 21.8% and 25.5% higher identification accuracy values over the other two networks, VGGnet-16 and ResNet-18. The findings of this study advance the potential application of the proposed approach towards expert-level condition identification based on limited real-world training data

Seismic Design of Offshore Wind Turbines: Good, Bad and Unknowns

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-08, pub-electronic 2021-06-12Publication status: PublishedLarge scale offshore wind farms are relatively new infrastructures and are being deployed in regions prone to earthquakes. Offshore wind farms comprise of both offshore wind turbines (OWTs) and balance of plants (BOP) facilities, such as inter-array and export cables, grid connection etc. An OWT structure can be either grounded systems (rigidly anchored to the seabed) or floating systems (with tension legs or catenary cables). OWTs are dynamically-sensitive structures made of a long slender tower with a top-heavy mass, known as Nacelle, to which a heavy rotating mass (hub and blades) is attached. These structures, apart from the variable environmental wind and wave loads, may also be subjected to earthquake related hazards in seismic zones. The earthquake hazards that can affect offshore wind farm are fault displacement, seismic shaking, subsurface liquefaction, submarine landslides, tsunami effects and a combination thereof. Procedures for seismic designing OWTs are not explicitly mentioned in current codes of practice. The aim of the paper is to discuss the seismic related challenges in the analysis and design of offshore wind farms and wind turbine structures. Different types of grounded and floating systems are considered to evaluate the seismic related effects. However, emphasis is provided on Tension Leg Platform (TLP) type floating wind turbine. Future research needs are also identified

Damage identification in concrete structures with uncertain but bounded measurements

The major sources of error in the measurements of concrete structures are the gauge sensitivities, calibration accuracies, amplitude linearities, and temperature corrections to the gauge sensitivities, which are given in terms of plus-minus ranges, and the round off errors in the measured responses, which are better represented by interval bounds. An algorithm is proposed adapting the existing modified Metropolis Hastings algorithm for estimating the posterior probability of the damage indices as well as the posterior probability of the bounds of the interval parameters, when the measurements are given in terms of interval bounds. A damage index is defined for each element of the finite element model considering the parameters of each element are intervals. Methods are developed for evaluating response bounds in the finite element software ABAQUS, when the parameters of the finite element model are intervals. The proposed method is validated against reinforced concrete beams with three damage scenarios mainly (1) loss of stiffness, (2) loss of mass, and (3) loss of bond between concrete and reinforcement steel, which have been tested in our laboratory. Comparison of the prediction from the proposed method with those obtained from Bayesian analysis and interval optimization technique show improved accuracy and computational efficiency in addition to better representation of measurement uncertainties through interval bounds

Deep Convolutional Neural Network for Condition Identification of Connections in Steel Structures

The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice

Deep Convolutional Neural Network for Condition Identification of Connections in Steel Structures

The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice

Condition identification of bolted connections using a virtual viscous damper

Vibration-based condition identification of bolted connections can benefit the effectivemaintenance and operation of steel structures. Existing studies show that modalparameters are not sensitive to such damage as loss of preload. In contrast, structuralresponses in the time domain contain all the information regarding a structuralsystem. Therefore, this study aims to exploit time-domain data directly for conditionidentification of bolted connection. Finite element (FE) model updating is carried outbased on the vibration test data of a steel frame, with various combinations of boltswith loss of preload, representing different damage scenarios. It is shown that thematch between the numerically simulated and measured acceleration responses ofthe steel frame cannot be achieved. The reason is that time-dependent nonlinearityis generated in bolted connections during dynamic excitation of the steel frame. Tocapture the nonlinearity, a virtual viscous damper is proposed. By using the proposeddamper alongside the updated system matrices of the FE model, the time domainacceleration responses are estimated with great consistency with the measuredresponses. The results demonstrate that the proposed virtual damper is not onlyeffective in estimating the time domain acceleration responses in each damage case,but also has the potential for condition identification of bolted connections with suchsmall damage as just one bolt with loss of preload. It can also be applied to otherchallenging scenarios of condition identification, where modal parameters are notsensitive to the damage

SHMnet: Condition assessment of bolted connection with beyond human-level performance

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet

Deep Convolutional Neural Network for Condition Identification of Connections in Steel Structures

The deep learning technologies have transformed many research areas with accuracy levels that the traditional methods are not comparable with. Recently, they have received increasing attention in the structural health monitoring (SHM) domain. In this paper, we aim to develop a new deep learning algorithm for structural condition monitoring and to evaluate its performance in a challenging case, bolt loosening damage in a frame structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN) is introduced. Second, a series of impact hammer tests are conducted on a steel frame in the laboratory under ten scenarios, with bolts loosened at different locations and quantities. For each scenario, ten repeated tests are performed to provide enough training data for the algorithm. Third, the algorithm is trained with different quantities of training data (from one to seven test data for each scenario), and then is tested with the rest test data. The results show that the proposed 1D-CNN with three convolutional layers provide reliable identification results (over 95% accuracy) with sufficient training data sets. It has the potential to transform the SHM practice

Seismic Design of Offshore Wind Turbines: Good, Bad and Unknowns

Large scale offshore wind farms are relatively new infrastructures and are being deployed in regions prone to earthquakes. Offshore wind farms comprise of both offshore wind turbines (OWTs) and balance of plants (BOP) facilities, such as inter-array and export cables, grid connection etc. An OWT structure can be either grounded systems (rigidly anchored to the seabed) or floating systems (with tension legs or catenary cables). OWTs are dynamically-sensitive structures made of a long slender tower with a top-heavy mass, known as Nacelle, to which a heavy rotating mass (hub and blades) is attached. These structures, apart from the variable environmental wind and wave loads, may also be subjected to earthquake related hazards in seismic zones. The earthquake hazards that can affect offshore wind farm are fault displacement, seismic shaking, subsurface liquefaction, submarine landslides, tsunami effects and a combination thereof. Procedures for seismic designing OWTs are not explicitly mentioned in current codes of practice. The aim of the paper is to discuss the seismic related challenges in the analysis and design of offshore wind farms and wind turbine structures. Different types of grounded and floating systems are considered to evaluate the seismic related effects. However, emphasis is provided on Tension Leg Platform (TLP) type floating wind turbine. Future research needs are also identified