Nonlinear autoregressive with exogenous input neural network for structural damage detection under ambient vibration

Time-series method has become of interest in damage detection, particularly for automated and continuous structural health monitoring. In comparison to the commonly used method based on modal data, time-series method offers a straightforward application due to having no requirement for modal analysis. Sensor clustering has been proven effective in improving the ability of time-series method to detect, locate and quantify damage. However, most of the applications rely on free vibration response that can be obtained directly by impact testing, which is difficult to practice for in-service structures, or indirectly by transforming the ambient vibration response. Therefore, a reliable method that allows direct utilisation of ambient vibration response for damage detection in structures without any data transformation is proposed in this study. The implementation of the proposed response-only method involves a three-stage procedure; (i) sensor clustering, (ii) time-series modelling and (iii) damage detection. Each sensor cluster is represented by a time-series model called nonlinear autoregressive with exogenous inputs (NARX) model, which is developed via artificial neural network (ANN) using undamaged acceleration data. The model is then utilised for predicting the damaged response and the difference between prediction errors is used to extract damage sensitive feature (DSF). The existence of uncertainties is addressed through setting up a damage threshold using several sets of undamaged data. The effectiveness of the method is demonstrated through a numerical slab model and experimental structures of reinforced concrete slabs and steel arches. It is found that the proposed structural damage detection approach based on NARX neural network is superior to linear ARX model as the approach is able to detect damage under ambient vibration. The results show that the highest predicted DSF corresponds to the location of damage and its value increases relatively with the severity of damage. Better damage detection is obtained when damage threshold is integrated into the proposed approach where the precision is increased by more than 24%. Overall, the proposed method is proven applicable to identify the existence, location and relative severity of structural damage under ambient vibration

Response surface methodology for damage detection using frequency and mode shapes

The model updating method is one popular method in vibration-based damage detection. However, the conventional model updating method requires a finite element (FE) model for sensitive computation during the iteration process, which leads to the problem of slow convergence and high time consumption. Therefore, the response surface methodology (RSM) has emerged as an alternative tool in FE model updating due to easy implementation and time-efficient processing where the computationally expensive analytical FE model is replaced by the simple and inexpensive response surface (RS) model. A recent RSM application in structural damage detection employs frequency as the sole response feature, limiting its ability to localise the existence of damage due to the inability of the frequency to ascertain damage in a symmetric structure. Therefore, a better RSM employing frequency and mode shapes as the response features is proposed in this study, as both parameters are proven sensitive to damage location. The implementation of the proposed method involves a three-phase procedure; (i) sampling, (ii) RS modelling and (iii) model updating. In order to develop the best RS model, two major parameters in the sampling stage, design of experiments (DOEs) and design spaces are carefully assessed through a series of sensitivity studies based on their damage detectability. The applicability of the technique is applied to detect simulated damage in numerical models of simply supported beam and steel frame structures as well as a laboratory tested steel portal frame. The results from sensitivity studies show that central composite design (CCD) with more sampling points in a small design space has better performance in detecting damages due to dense population of data which adequately represents the design space. The results from numerical study demonstrated that the proposed RSM method has a good ability to detect damage due to noise free data while results from experimental study depicted some false detections. It is concluded that the proposed method is reliable in damage detection provided that the data has good precision. Nevertheless, the presence of noise and errors in real practice are inevitable, thus pollute the measured data. Therefore, it is suggested to incorporate the effect of uncertainties in the proposed RSM to improve its applicability in real practice

Efficient Calculation and Visualization of Energy Grade Line (EGL) and Hydraulic Grade Line (HGL) in Fluid Flow Systems

The goal of this project is to use MATLAB software to efficiently calculate and visually represent the Energy Grade Line (EGL) and Hydraulic Grade Line (HGL) in fluid flow systems. This work serves as a practical showcase of our how MATLAB can be used to make Bernoulli calculations in the realm of civil engineering. In addition, the incorporation of Building Information Modeling (BIM) offers the potential to further enhance the design and analysis of fluid flow systems. The Energy Grade Line (EGL) holds a vital position in fluid mechanics, acting as a representation of a fluid's energy as it moves through a conduit. This blend of pressure, velocity, and elevation energy provides crucial insights into the behavior and characteristics of the fluid throughout its course. For engineers, the EGL is an essential tool, aiding in the analysis of fluid flow systems, identification of energy losses, and optimization of hydraulic structures. Furthermore, the Hydraulic Grade Line (HGL) represents another critical concept in fluid mechanics, graphically illustrating the pressure head of fluid along a specified route. It offers a clear depiction of energy distribution within a fluid flow system, accounting for the sum of pressure and elevation heads. Engineers heavily rely on the HGL to inspect pressure fluctuations, detect potential issues like pressure drops or excessive velocities, and make informed decisions to ensure fluid flow's efficiency and reliability. Thorough testing and implementation revealed that, with precise configuration of the MATLAB environment code, our code achieves high accuracy calculations for Energy Grade Line (EGL) and Hydraulic Grade Line (HGL) values. This setup also enables the generation of clear graphical representations, providing engineers with a reliable and visually accessible graph for fluid flow system analysis

Performance of steel-bolt-connected industrialized building system frame subjected to hydrodynamic force

People need durable shelters for living safely due to devastation caused by flooding in some areas, and it is not easy to mitigate the frequency and intensity of the flooding. Therefore, in this research, an industrialized building system (IBS) has been proposed as one of the best solutions. However, most of the existing IBSs were not designed and tested for resisting a sudden horizontal impact. Furthermore, the joints of some IBSs would likely be vulnerable to failure when subjected to a horizontal impact. There is a need to develop a bolt-connected IBS that is able to withstand a horizontal impact load. Thus, this study aimed to investigate the performance of steel-bolt-connected IBS frames subjected to the sudden impact of hydrodynamic force. Autodesk computational fluid dynamic (CFD) simulation was used for optimizing the laboratory experiment. A 1:5-scale IBS frame was designed and tested for the dam-break test using 1 m, 2 m, and 3 m reservoir water levels. The results showed that the bolt connections were very effective and robust in the IBS frame. They also restricted damages from spreading to other structural components due to energy dissipation. The main findings of this study are crucial to improving the current IBS method of construction

Sensor clustering-based approach for structural damage identification under ambient vibration

This study explored the sensor clustering-based damage detection beyond the free-vibration limitation to allow for the direct utilisation of time-series for damage identification under ambient vibration. In the proposed method, a dense sensor network is clustered and each sensor cluster is represented by nonlinear autoregressive with exogenous inputs (NARX) model, which is developed in a black-box manner via an artificial neural network. Damage detection is achieved through a new damage sensitive feature which is formulated from the NARX neural network prediction error. The efficiency of the proposed methodology is assessed firstly using test data of an 8-DOF system and later by conducting an experimental study on scaled steel arch laboratory models subjected to various damage cases. The obtained results reveal that the proposed method can satisfactorily detect, localise, and estimate damage severity in the test structure

Response surface methodology for damage detection using frequency and mode shape

Response surface methodology (RSM) has been proven applicable for updating finite element baseline models. Most RSM-based damage detection methods are frequency-based, limiting their application to small structures and symmetrical damage. This study therefore proposed a new RSM method that employs both natural frequencies and mode shapes. The efficiency of the proposed damage detection method is demonstrated through a numerical model of a simply supported beam and a laboratory-tested steel frame. To choose the best RSM design for damage detection, the effects of design of experiment (DOE) to the damage detectability is investigated. The DOEs studied include central composite design (CCDMRV and CCD64), Box-Behnken design (BBD) and D-optimal design (Dopt). The results show that RSM is a potentially useful approach for damage detection. The RS model based on the CCD sampling method with bigger sample size provides the best damage detection performance

Assessments of Acoustical Performance of Classrooms and Teachers’ Acoustical Comfort in The School Environment – A Case Study

In recent times, damage identification based on vibration methods are emerging as common approaches, in which the techniques apply the vibration response of a monitored structure, such as modal frequencies and damping ratios, to evaluate its condition and detect structural damage. The basis of the vibration-based health monitoring method is that when there are alterations in the physical characteristics of a structure, there will also be changes in its vibration properties. This paper proposed a neuro-fuzzy artificial intelligence method, called adaptive neuro-fuzzy inference system (ANFIS), to detect damage using modal properties. To generate the modal characteristics of the structures, experimental study and finite element analysis of I-beams with single damage cases were performed. The results showed that the ANFIS approach was able to detect the magnitude and location of the damage with a significant degree of precision, and notably reduced computational time

Assessments of Acoustical Performance of Classrooms and Teachers’ Acoustical Comfort in The School Environment – A Case Study

In recent times, damage identification based on vibration methods are emerging as common approaches, in which the techniques apply the vibration response of a monitored structure, such as modal frequencies and damping ratios, to evaluate its condition and detect structural damage. The basis of the vibration-based health monitoring method is that when there are alterations in the physical characteristics of a structure, there will also be changes in its vibration properties. This paper proposed a neuro-fuzzy artificial intelligence method, called adaptive neuro-fuzzy inference system (ANFIS), to detect damage using modal properties. To generate the modal characteristics of the structures, experimental study and finite element analysis of I-beams with single damage cases were performed. The results showed that the ANFIS approach was able to detect the magnitude and location of the damage with a significant degree of precision, and notably reduced computational time