Condition assessment of RC beams using artificial neural networks

Krishna, B. Murali; Reddy, V. Guru Prathap; Shafee, Mohammed; Tadepalli, T.

doi:10.1016/j.istruc.2019.09.014

Cited by 19 publications

(4 citation statements)

References 13 publications

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“…Regarding the hidden layers, it is worth noting that, in general, one hidden layer is sufficient. However, when the number of neurons in a single layer increases, the predictive efficiency does not increase, and for complex problems, two hidden layers could be required [10,17]. After a trial-and-error stage with one hidden layer and with a neuron number increase, the networks do not converge.…”

Section: Implementation Of the Anns: Design And Selection Of The Ann mentioning

confidence: 99%

“…In civil engineering, ANNs have been successfully used in automation and optimization [2,5,8], in material formulation [9], and in system identification and monitoring [10,11]. In structural analysis and design, the following applications could be highlighted [2,6,12]: structural analysis of systems with large degrees of freedom, size optimization of structural members, joint location, shape optimization of structural types (e.g., truss geometry), topology optimization (based on deletion of ineffective structural members), and maximum stress identification and location.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

2020

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In the built environment, one of the main concerns during the design stage is the selection of adequate structural materials and elements. A rational and sensible design of both materials and elements results not only in economic benefits and computing time reduction, but also in minimizing the environmental impact. Nowadays, Artificial Neural Networks (ANNs) are showing their potential as design tools. In this research, ANNs are used in order to foster the implementation of efficient tools to be used during the early stages of structural design. The proposed networks are applied to a dry precast concrete connection, which has been modelled by means of the Finite Element Method (FEM). The parameters are: strength of concrete and screws, diameter of screws, plate thickness, and the posttensioning load. The ANN input data are the parameters and nodal stresses obtained from the FEM models. A multilayer perceptron combined with a backpropagation algorithm is used in the ANN architecture, and a hyperbolic tangent function is applied as an activation function. Comparing the obtained predicted stresses to those of the FEM analyses, the difference is less than 9.16%. Those results validate their use as an efficient structural design tool. The main advantage of the proposed ANNs is that they can be easily and effectively adapted to different connection parameters. In addition, their use could be applied both in precast or cast in situ concrete connection design.

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Section: Implementation Of the Anns: Design And Selection Of The Ann mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

2020

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“…With the continuous improvement in social infrastructure construction techniques, structural safety is being confronted with the challenges of large volumes, complex structures, and harsh environments [1][2][3]. This can result in complications, such as delayed structural damage diagnoses and inaccurate safety evaluations.…”

Section: Introductionmentioning

confidence: 99%

Safety Evaluation of Reinforced Concrete Structures Using Multi-Source Fusion Uncertainty Cloud Inference and Experimental Study

Liu,

Guo,

Zhang

2023

Sensors

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Structural damage detection and safety evaluations have emerged as a core driving force in structural health monitoring (SHM). Focusing on the multi-source monitoring data in sensing systems and the uncertainty caused by initial defects and monitoring errors, in this study, we develop a comprehensive method for evaluating structural safety, named multi-source fusion uncertainty cloud inference (MFUCI), that focuses on characterizing the relationship between condition indexes and structural performance in order to quantify the structural health status. Firstly, based on cloud theory, the cloud numerical characteristics of the condition index cloud drops are used to establish the qualitative rule base. Next, the proposed multi-source fusion generator yields a multi-source joint certainty degree, which is then transformed into cloud drops with certainty degree information. Lastly, a quantitative structural health evaluation is performed through precision processing. This study focuses on the numerical simulation of an RC frame at the structural level and an RC T-beam damage test at the component level, based on the stiffness degradation process. The results show that the proposed method is effective at evaluating the health of components and structures in a quantitative manner. It demonstrates reliability and robustness by incorporating uncertainty information through noise immunity and cross-domain inference, outperforming baseline models such as Bayesian neural network (BNN) in uncertainty estimations and LSTM in point estimations.

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“…Unfortunately, the vibrational responses acquired in a civil structure generally present nonlinear and non-stationary properties, besides having a low SNR (high-level noise), compromising negatively the results obtained by FFT to evaluate the health condition of a civil structure [25]. For this reason, in recent years, diverse methods have been proposed in the literature such as Kalman filter approaches [33], Hilbert-Huang transform (HHT) [34][35][36], time series autoregressive (AR) models [10,[37][38][39], wavelet transform-based algorithms (WT) [40,41], artificial neural networks (ANN) [11,28,[42][43][44], probabilistic-based approaches [15,18,45,46], subspace methods [12,47,48], WT-NN [49][50][51][52] and deep learning methods [53][54][55][56], among other methods or strategies. Although they have shown promising results in evaluating the condition of civil structures, these methods also present problems in identifying reliable features in noisy signals when associating them to the structure condition; in addition, some of them require repeated processing and modeling, the hand-crafted selection of the best-suited parameters, a large database, and multiple indices to detect different types of damage [57].…”

Section: Introductionmentioning

confidence: 99%

Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals

Perez-Ramirez

Machorro-Lopez²,

Valtierra-Rodríguez

et al. 2020

Mathematics

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A new multiple signal classification (MUSIC)-based methodology is presented for detecting and locating multiple damage types in a truss-type structure subjected to dynamic excitations. The methodology is based mainly on two steps: in step 1, the MUSIC method is employed to obtain the pseudo-spectra of vibration signatures, healthy and damaged, to be used for damage detection. In step 2, a new damage index, based on the obtained pseudo-spectra, is proposed to measure the structure condition. Furthermore, the damage location is estimated according to the variation in the amplitudes of the estimated pseudo-spectra. The presented results show that the proposed methodology can make an accurate and reliable estimation of the condition and location of three specific damage conditions, i.e., loosened bolts, internal corrosion, and external corrosion.

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Condition assessment of RC beams using artificial neural networks

Cited by 19 publications

References 13 publications

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

Safety Evaluation of Reinforced Concrete Structures Using Multi-Source Fusion Uncertainty Cloud Inference and Experimental Study

Location of Multiple Damage Types in a Truss-Type Structure Using Multiple Signal Classification Method and Vibration Signals

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