Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

In this paper, a new sensor chip with frequency reconstruction range of 2.252 GHz ~ 2.450 GHz is designed and fabricated. On this basis, a self-designed "T-shaped" shell is added to overcome the disadvantage of uneven deformation of the traditional steel shell, and the range of the sensor chip is expanded to 0 kN ~ 96 kN. The liquid metal antenna is used to carry out a step-by-step loading test, and the relationship between the antenna resonance frequency and the pressure load is analyzed. The results show that there is a good linear relationship between the pressure load and the resonant frequency. Therefore, the liquid metal antenna can be regarded as a pressure sensor. The cyclic loading and unloading experiments of the sensor are carried out, and different loading rates are used to explore the influence on the performance of the sensor. The loading and unloading characteristic curves and the influence characteristic curves of loading rate are plotted. The experimental results show that the sensor has no residual deformation during the cycle of loading and unloading. Moreover, the influence of temperature on the performance of the sensor is studied, and the temperature correction formula is derived.

A novel impact localization method is presented based on impact induced elastic waves in sensorized composite structure subjected to temperature fluctuations. In real practices, environmental and operational conditions influence the acquired signals and consequently make the feature (particularly Time of Arrival (TOA)) extraction process, complicated and troublesome. To overcome this complication, a robust TOA estimation method is proposed based on the times in which the absolute amplitude of the signal reaches to a specific amplitude value. The presented method requires prior knowledge about the normalized wave velocity in different directions of propagation. To this aim, a finite element model of the plate was built in ABAQUS/CAE. The impact location is then highlighted by calculating an error value at different points of the structure. The efficiency of the developed impact localization technique is experimentally evaluated by dropping steel balls with different energies on a carbon fiber composite plate with different temperatures. It is demonstrated that the developed technique is able to localize impacts with different energies even in the presence of noise and temperature fluctuations.

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

It is essential to monitor the working conditions of bolt-connected joints, which are widely used in various kinds of steel structures. The looseness of bolts may directly affect the stability and safety of the entire structure. In this study, a guided wave-based method for bolt looseness detection and localization is presented for a joint structure with multiple bolts. SH waves generated and received by a small number (two pairs) of magnetostrictive transducers were used. The bolt looseness index was proposed based on the changes in the reconstructed responses excited by the time reversal signals of the measured unit impulse responses. The damage locations and local damage severities were estimated using the damage indices from several wave propagation paths. The back propagation neural network (BPNN) technique was employed to identify the local damages. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the total damage severity can be successfully detected under the effect of external force and measurement noise. The local damage severity can be estimated reasonably for the experimental data using the BPNN constructed by the training patterns generated from the finite element simulations.

Monitoring large structures is a significant issue involving public health on which new studies are constantly carried out. Although the Global Navigation Satellite System (GNSS) is the most preferable method for measuring structural displacements, total stations, one of the classical geodetic instruments, are the first devices that come to mind in cases that require complementary usage and auxiliary measurement methods. In this study, the relative displacements of the structural movements of a tower were determined using robotic total stations (RTS) and GNSS. Two GNSS receivers and two RTS observations were carried out simultaneously for 10 hours under normal weather conditions. The spectral analysis of the GNSS data was performed using fast Fourier transform (FFT), and while the dominant modal frequencies were determined, the total station data were balanced with the least-squares technique, and the position and position errors were calculated for each measurement epoch. It has been observed that low-frequency structural movements can be determined by both methods. This result shows that total station measurements are a helpful alternative method for monitoring large structures in situations where measurements are not possible due to the basic handicaps of GNSS or where it is necessary to determine displacements with short observations.

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.