A methodology based on distributed fiber optic sensors is proposed to detect the lateral buckling for subsea pipelines in this study. Uncontrolled buckling may lead to serious consequences for the structural integrity of a pipeline. A simple solution to this problem is to control the formation of lateral buckles among the pipeline. This firms the importance of monitoring the occurrence and evolution of pipeline buckling during the installation stage and long-term service cycle. This study reports the experimental investigations on a method for distributed detection of lateral buckling in subsea pipelines with Brillouin fiber optic sensor. The sensing scheme possesses the capability for monitoring the pipeline over the entire structure. The longitudinal strains are monitored by mounting the Brillouin optical time domain analysis (BOTDA) distributed sensors on the outer surface of the pipeline. Then the bending-induced strain is extracted to detect the occurrence and evolution of lateral buckling. Feasibility of the method was validated by using an experimental program on a small scale model pipe. The results demonstrate that the proposed approach is able to detect, in a distributed manner, the onset and progress of lateral buckling in pipelines. The methodology developed in this study provides a promising tool for assessing the structural integrity of subsea pipelines.

In this study, a structural identification method is proposed to assess the integrity of gravity-type caisson structures by analyzing vibration features. To achieve the objective, the following approaches are implemented. Firstly, a simplified structural model with a few degrees-of-freedom (DOFs) is formulated to represent the gravity-type caisson structure that corresponds to the sensors' DOFs. Secondly, a structural identification algorithm based on the use of vibration characteristics of the limited DOFs is formulated to fine-tune stiffness and damping parameters of the structural model. Finally, experimental evaluation is performed on a lab-scaled gravity-type caisson structure in a 2-D wave flume. For three structural states including an undamaged reference, a water-level change case, and a foundation-damage case, their corresponding structural integrities are assessed by identifying structural parameters of the three states by fine-tuning frequency response functions, natural frequencies and damping factors.

Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for an offshore structure, there are few cases to apply a structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure in Korea under changes in temperature and transverse loadings. Principal component analysis (PCA)-based approach was applied with a conventional damage index to eliminate environmental changes by removing principal components sensitive to them. Experimental results showed that the proposed approach is an effective tool for long-term SHM under significant environmental changes.

Submersed rock-berm structures are frequently used for protection of underwater lifelines such as pipelines and power cables. During the service life, the rock-berm structure can experience several accidental loads such as anchor collision. The consequences can be severe with a certain level of frequency; hence, the structural responses should be carefully understood for implementing a proper structural health monitoring method. However, no study has been made to quantify the structural responses because it is hard to deal with the individual behavior of each rock. Therefore, this study presents a collision analysis of the submersed rock-berm structure using a finite element software package by facilitating the smoothed-particle hydrodynamics (SPH) method. The analysis results were compared with those obtained from the Lagrange method. Moreover, two types of anchors (stock anchor and stockless anchor), three collision points and two different drop velocities (terminal velocity of each anchor and 5 m/s) were selected to investigate the changes in the responses. Finally, the effect of these parameters (analysis method, anchor type, collision point and drop velocity) on the analysis results was studied. Accordingly, the effectiveness of the SPH method is verified, a safe rock-berm height (over 1 m) is proposed, and a gauge point (0.5 m above the seabed) is suggested for a structural health monitoring implementation.

A main goal of this study is to propose a damage detection technique to detect and localize damages of a top-tensioned riser. In this paper, the top-tensioned finite element (FE) model is considered as an analytical model of the riser, and a vibration-based damage detection method is proposed. The present method consists of a FE model updating and damage index method. In order to accomplish the goal of this study, first, a sensitivity-based FE model updating method using natural frequencies and zero frequencies is introduced. Second, natural frequencies and zero frequencies of the axial mode on the top-tensioned riser are estimated by eigenvalue analysis. Finally, the locations and severities of the damages are estimated from the damage index method. Three numerical examples are considered to verify the performance of the proposed method.

Previous long-term measurements of the Uldolmok tidal current power plant showed that the structure's natural frequencies fluctuate with a constant cycle-i.e., twice a day with changes in tidal height and tidal current velocity. This study aims to improve structural health monitoring (SHM) techniques for offshore structures under a harsh tidal environment like the Uldolmok Strait. In this study, lab-scale experiments on a simplified offshore structure as a lab-scale test structure were conducted in a circulating water channel to thoroughly investigate the causes of fluctuation of the natural frequencies and to validate the displacement estimation method using multimetric data fusion. To this end, the numerical study was additionally carried out on the simplified offshore structure with damage scenarios, and the corresponding change in the natural frequency was analyzed to support the experimental results. In conclusion, (1) the damage that occurred at the foundation resulted in a more significant change in natural frequencies compared with the effect of added mass; moreover, the structural system became nonlinear when the damage was severe; (2) the proposed damage index was able to indicate an approximate level of damage and the nonlinearity of the lab-scale test structure; (3) displacement estimation using data fusion was valid compared with the reference displacement using the vision-based method.

An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little.

In this study, local dynamic characteristics of mountable PZT interfaces are numerically analyzed to verify their feasibility on impedance monitoring of the prestress-loss in tendon anchorage subsystems. Firstly, a prestressed tendon-anchorage system with mountable PZT interfaces is described. Two types of mountable interfaces which are different in geometric and boundary conditions are designed for impedance monitoring in the tendon-anchorage subsystems. Secondly, laboratory experiments are performed to evaluate the impedance monitoring via the two mountable PZT interfaces placed on the tendon-anchorage under the variation of prestress forces. Impedance features such as frequency-shifts and root-mean-square-deviations are quantified for the two PZT interfaces. Finally, local dynamic characteristics of the two PZT interfaces are numerically analyzed to verify their performances on impedance monitoring at the tendon-anchorage system. For the two PZT interfaces, the relationships between structural parameters and local vibration responses are examined by modal sensitivity analyses.

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

Structural nonlinearity is a common phenomenon encountered in engineering structures under severe dynamic loading. It is necessary to localize and identify structural nonlinearities using structural dynamic measurements for damage detection and performance evaluation of structures. However, identification of nonlinear structural systems is a difficult task, especially when proper mathematical models for structural nonlinear behaviors are not available. In prior studies on nonparametric identification of nonlinear structures, the locations of structural nonlinearities are usually assumed known and all structural responses are measured. In this paper, an identification algorithm is proposed for locating and identifying model-free structural nonlinearities and systems using incomplete measurements of structural responses. First, equivalent linear structural systems are established and identified by the extended Kalman filter (EKF). The locations of structural nonlinearities are identified. Then, the model-free structural nonlinear restoring forces are approximated by power series polynomial models. The unscented Kalman filter (UKF) is utilized to identify structural nonlinear restoring forces and structural systems. Both numerical simulation examples and experimental test of a multi-story shear building with a MR damper are used to validate the proposed algorithm.

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

Modal identification of civil engineering structures based on ambient vibration measurement has been widely investigated in the past decades, and a variety of output-only operational modal identification methods have been proposed. However, vibration modes, even fundamental low-order modes, are not always identifiable for large-scale structures under ambient vibration excitation. The identifiability of vibration modes, deficiency in modal identification, and criteria to evaluate robustness of the identified modes when applying output-only modal identification techniques to ambient vibration responses were scarcely studied. In this study, the mode identifiability of the cable-stayed Ting Kau Bridge using ambient vibration measurements and the influence of the excitation intensity on the deficiency and robustness in modal identification are investigated with long-term monitoring data of acceleration responses acquired from the bridge under different excitation conditions. It is observed that a few low-order modes, including the second global mode, are not identifiable by common output-only modal identification algorithms under normal ambient excitations due to traffic and monsoon. The deficient modes can be activated and identified only when the excitation intensity attains a certain level (e.g., during strong typhoons). The reason why a few low-order modes fail to be reliably identified under weak ambient vibration excitations and the relation between the mode identifiability and the excitation intensity are addressed through comparing the frequency-domain responses under normal ambient vibration excitations and under typhoon excitations and analyzing the wind speeds corresponding to different response data samples used in modal identification. The threshold value of wind speed (generalized excitation intensity) that makes the deficient modes identifiable is determined.

Thermal loads, especially thermal gradients, have a considerable effect on the behaviors of large-scale bridges throughout their lifecycles. Bridge design specifications provide minimal guidance regarding thermal gradients for simple bridge girders and do not consider transversal thermal gradients in wide girder cross-sections. This paper investigates the three-dimensional thermal gradients of arch bridge girders by integrating long-term field monitoring data recorded by a structural health monitoring system, with emphasis on the vertical and transversal thermal gradients of wide concrete-steel composite girders. Based on field monitoring data for one year, the time-dependent characteristics of temperature and three-dimensional thermal gradients in girder cross-sections are explored. A statistical analysis of thermal gradients is conducted, and the probability density functions of transversal and vertical thermal gradients are estimated. The extreme thermal gradients are predicted with a specific return period by employing an extreme value analysis, and the profiles of the vertical thermal gradient are established for bridge design. The transversal and vertical thermal gradients are developed to help engineers understand the thermal behaviors of concrete-steel composite girders during their service periods.

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.