Damage Assessment of Reinforced Concrete Structures: A Review

Damage may be assessed using several damage indices with values associated with different structural damage states. The usefulness of a variety of current response-based damage indices in seismic damage assessment is addressed and critically assessed. A novel rational damage assessment method is provided, which measures the structure’s physical reaction characteristics. A practical method based on various analyses is given to evaluate the damaged structures in earthquakes of different intensities. This paper provides an overview of previous research works on the damage assessment of the reinforced concrete structures. This study may be helpful for easy understanding about the damage assessment of reinforced concrete structures and reduce the impacts of disaster and surrounding structures.

Damage Assessment of Reinforced Concrete Structures: A Review

Damage may be assessed using several damage indices with values associated with different structural damage states. The usefulness of a variety of current response-based damage indices in seismic damage assessment is addressed and critically assessed. A novel rational damage assessment method is provided, which measures the structure’s physical reaction characteristics. A practical method based on various analyses is given to evaluate the damaged structures in earthquakes of different intensities. This paper provides an overview of previous research works on the damage assessment of the reinforced concrete structures. This study may be helpful for easy understanding about the damage assessment of reinforced concrete structures and reduce the impacts of disaster and surrounding structures.

Structural Seismic Damage Assessment Method Based on Structural Dynamic Characteristics

The seismic damage state of building structure can be evaluated by observing the fundamental period change of structure. Firstly, the fundamental period calculation formula that adapts to the deformation pattern and distribution mode of horizontal seismic action for reinforced concrete frame structure is derived. Secondly, the seismic damage assessment standard of building structure considering period variation is established. Then, the seismic damage assessment method of building structure is constructed. Finally, the seismic damage example is used to verify the established evaluation method. The results show that the established research method has high accuracy and good engineering practicability.

Synchronized Assessment of Bridge Structural Damage and Moving Force via Truncated Load Shape Function

Moving load and structural damage assessment has always been a crucial topic in bridge health monitoring, as it helps analyze the daily operating status of bridges and provides fundamental information for bridge safety evaluation. However, most studies and research consider these issues as two separate problems. In practice, unknown moving loads and damage usually coexist and influence the bridge vibration synergically. This paper proposes an innovative synchronized assessment method that determines structural damages and moving forces simultaneously. The method firstly improves the virtual distortion method, which shifts the structural damage into external virtual forces and hence transforms the damage assessment as well as the moving force identification to a multi-force reconstruction problem. Secondly, a truncated load shape function (TLSF) technique is developed to solve the forces in the time domain. As the technique smoothens the pulse function via a limited number of TLSF, the singularity and dimension of the system matrix in the force reconstruction is largely reduced. A continuous beam and a three-dimensional truss bridge are simulated as examples. Case studies show that the method can effectively identify various speeds and numbers of moving loads, as well as different levels of structural damages. The calculation efficiency and robustness to white noise are also impressive.

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

Correction: Park, S.; Jung, K. Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment. ISPRS Int. J. Geo-Inf. 2021, 10, 574

Missing Acknowledgments [...]

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.