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Structural failure can be prevented if the damage in the structure is detected at its onset and appropriate retrofitting carried out. Towards this end, this paper presents a vibration-based technique, using only the first vibration mode, for predicting damage and its location and severity in steel beams that are important structural components in buildings and bridges. For single damage scenarios, the modal strain energy based damage index β was capable of detecting, locating and quantifying damage. For multiple damage scenarios, Artificial Neural Network incorporating β as the input layer was used. This research used computer simulations supported by limited experiments. Damage intensity was specified as a percentage reduction in stiffness compared to that at first yield. The procedure is illustrated through several numerical examples and the results confirm the feasibility of the method and its application in preventing structural failure. Keywords: Damage prediction, Failure prevention, Vibration based technique, Modal strain energy, Artificial Neural Network, Damage location, Damage severity, Damage index, Damage scenarios.

Damage in a structure is defined as changes to its geometric and material properties, leading to a reduction in the structural stiffness which negatively affects the performance of the structure. Reduction in the structural stiffness produces changes in the modal parameters such as the natural frequencies and mode shapes. Artificial neural networks (ANNs) have been applied extensively in recent years due to their excellent pattern recognition ability that is useful for structural damage identification purposes. In this paper, ANNs based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations were applied to generate dynamic parameters of the first five flexural modes of structures. In damage identification using ANNs, five individual networks corresponding to mode 1 to mode 5 were trained, and then a method based on neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble network has the advantages of all the individual networks from different vibrational modes. The results showed that ensemble neural networks have a strong potential for structural damage identification.

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

This study presents and reviews the technical literature and previous studies for the past three decades on structural damage identification using ANNs and measured FRFs as inputs. Much of the previous studies have used modal parameters to ascertain the success of damage identification. However, significant information may not be properly represented through the application of modal parameters. With this in mind, the direct use of frequency domain data in terms of the Frequency Response Functions (FRFs) seems more appropriate. Recent studies indicate that ANNs can be trained on measured FRFs of healthy and damaged models of structure to assess the condition of the structure. According to this review, it is clear that there have been numerous studies which have gone on to apply the ANNs on FRF data of structures in the field of damage identification and it has been shown that ANNs using FRFs can provide several advantages over the modal parameters and damage identification has subsequently become much improved.

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

Damage in structures can negatively affect their functionality and safety and leads to failure. Thus it is very important to monitor structures for occurrence, location and severity of damage. Structural health monitoring techniques provide information on the life expectancy of structures simultaneously detects and locates structural damage. Damage identification of structures based on vibration has always been important subjects and are being rapidly used to damage and location of structures. Artificial Neural Networks (ANNs) as a soft computing method using dynamic parameters of structures have been utilized increasingly for structural damage detection due to their excellent pattern recognition capability. Dynamic parameters of structure are easy to implement for damage identification and can directly linked to the topology of structure. This study presents the application of ANN for damage identification in steel beams using dynamic parameters. For identification of severity and location of damage, at first, five individual neural networks corresponding to mode 1 to mode 5 are considered. At the second step, a method based on neural network ensemble is proposed to combine the outcomes of the individual neural networks to a single network. Ensemble results were evaluated and discussed according to the differences between predicted output by ANN and desire data (target data) obtained from experimental modal analysis of structure.

Structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus, it is important to monitor structures for the occurrence, location and extent of damage. Artificial neural networks (ANNs) as a numerical technique have been applied increasingly for damage identification with varied success. ANNs are inspired by human biological neurons and have been used to model some specific problems in many areas of engineering and science to achieve reasonable results. ANNs have the ability to learn from examples and then adapt to changing situations when sufficient input-output data are available. This paper presents the application of ANNs for detection of damage in a steel girder bridge using natural frequencies as dynamic parameters. Dynamic parameters are easy to implement for damage assessment and can be directly linked to the topology of structure. In this study, the required data for the ANNs in the form of natural frequencies will be obtained from experimental modal analysis. This paper also highlights the concept of ANNs followed by the detail presentation of the experimental modal analysis for natural frequencies extraction. Key words: Artificial neural networks (ANNs), back propagation (BP), damage identification, natural frequency.

The use of 3D seismic attributes for predicting reservoir properties away from the well bore has been used routinely in the industry. Recently a study utilizing multi attribute analysis and neutral network technique applied to one of the Marrat reservoirs in west Kuwait has not only described the reservoir geometry but has also opened up new areas for exploration. Further, the seismic derived porosity volume has been also integrated with the geological model for future well placement. The Middle Marrat limestone reservoir of Jurassic age in the Dharif field is one of the major oil producers in the area. This field discovered in 1988, is an elongated anticline trending NNE-SSW, with a major fault to the west. The reservoir thickness varies from 50-230ft and porosities ranging from 12 to 20%. Since a Pilot water injection program is being initiated, a good reservoir description would be essential for planning a successful injection program. The seismic derived porosity volume derived from neural network analysis has been a key in identifying inter-well areas as well as regions away from the wells with good porosity which is consistent with the available geological information. Incorporating the porosity volume as a “soft constraint” to the available geological model has further refined the model and is expected to assist in effective placement of future wells.