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Conventional model updating methods for structures mainly use modal parameters, which are global structural responses such as natural frequency, mode shape, and modal damping obtained through vibration measurements. A model updated using those modal parameters can accurately estimate global structural responses and is used to evaluate the state of a structure. However, when using modal parameters only in model updating, a difficulty arises regarding safety assessment and response prediction of local structural members. To ensure safety of structural members, this study proposes a model updating method using the strains measured from the impact tests in structural members of steel frame in addition to modal parameters. In the model updating technique proposed in this study, error functions are set as the differences between measured and model’s modal parameters and the maximum strain subject to impact force, and the functions are minimized using a multi-objective optimization algorithm. The strain responses predicted in the updated model are used in the safety assessment of structural members in steel frames. The proposed method was experimentally verified through impact hammer loading tests on a planar steel moment frame. Furthermore, the stresses obtained from the model updated with modal parameters only and the model updated by the proposed method were compared.

Summary A fuzzy algorithm, the Takagi–Sugeno model, is implemented to develop a fuzzy inference system for predicting the holding capacity of suction caisson foundations for offshore platforms. The premise parameters of the fuzzy model are optimized by using a subtractive clustering algorithm. The consequent parameters are optimally determined via a weighted least square estimation. The input variables used for training the fuzzy model include the aspect ratio of the caisson, the undrained shear strength of the clay, and the angle that the chain force forms with the horizontal. The output of the proposed fuzzy model is the capacity of the suction caisson anchor. To demonstrate the effectiveness of the fuzzy modeling framework, the results of extensive finite element analyses are investigated. Comparisons of the trained fuzzy model with the data demonstrate that the proposed modeling framework is an effective method to estimate the holding capacity of offshore suction caisson systems. Moreover, the performance of the fuzzy model is robust against higher levels of input data uncertainties. Copyright © 2017 John Wiley & Sons, Ltd.

This paper proposes system identification models of smart concrete structures equipped with magnetorheological (MR) dampers under a variety of high impact loads. The proposed model was used to predict and analyze the highly nonlinear behavior of integrated structure-control systems subjected to impact loading. Highly nonlinear behavior of the integrated structure-MR damper was represented by a wavelet-based time delayed adaptive neuro-fuzzy inference system (W-TANFIS). To generate sets of input and output data for training and validating the proposed W-TANFIS models, experimental studies were performed on a smart reinforced concrete beam under a variety of impact loads. The impact forces and current signals on an MR damper were used as input signals for training the W-TANFIS to predict the acceleration, deflection, and strain responses. As a benchmark, an adaptive neuro-fuzzy inference system (ANFIS) was used. It was demonstrated that the proposed W-TANFIS framework is effective in anticipating the structural responses of the reinforced concrete beam-MR damper system subjected to impact loading. In addition, the comparison of the W-TANFIS and ANFIS models demonstrated that the W-TANFIS model has better performance over the ANFIS model.

This paper proposes a nonlinear autoregressive moving average (NARMA) model for use in system identification (SI) of high performance smart buildings under ambient excitations. The NARMA model is implemented by including the cross terms of output signals to a linear autoregressive moving average (LARMA) time series model. To demonstrate the effectiveness of the proposed NARMA approach, a three-story building equipped with smart control devices is investigated under a variety of ambient excitations. To access the robustness of the proposed model, it is tested under various levels of measurement noises. It is demonstrated from the extensive simulations that the proposed NARMA model is effective in predicting the ambient vibration responses of the high performance smart buildings with severe measurement noises.

This paper proposed the application of tuned mass damper (TMD) systems to bridge piers for structural impact damage mitigation to reduce the risk of collapses. A bridge superstructure and substructure were designed in accordance with The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) Bridge Design Specifications (BDS) (2012). A variety of vessel collision forces were obtained from collision testing of a scaled reinforced concrete pier. The optimal parameters of TMD systems were determined such that the drift and displacement of the bridge superstructure were minimized for various impact scenarios. Structural impact mitigation performance of the pier equipped with the proposed optimal TMD system was compared with four different TMD systems employing the benchmark TMD optimal parameters. The uncontrolled responses were used as a baseline. It was demonstrated from the extensive simulations that the control effectiveness of the proposed TMD system was 25% better than all of the existing TMD models in reducing the response of the structure.

Arsava, K.S., Kim, Y., and Kim, K.H., 2016. Automatic control for hazard mitigation of coastal infrastructures. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1037 - 1041. Coconut Creek (Florida), ISSN 0749-0208. This paper proposes to develop an acceleration feedback-based smart fuzzy controller for coastal bridge piers employing magnetorheological (MR) dampers. The proposed system reduces nonlinear structural responses to collision forces so that the risk of collapse is minimized. Based on the American Association of State Highway and Transportation Officials (AASHTO, 2012) specifications, a scaled coastal bridge pier was manufactured to mimic a full scale coastal bridge pier that has three columns and a pier cap. The scaled coastal bridge pier is equipped with an MR damper, accelerometers, an LVDT, strain gages, load cells, data acqui...

In this paper, a new biological modeling approach is proposed for predicting complex heterogeneous subcellular behaviors. Cell protrusion which initiates cell migration has a significant amount of subcellular heterogeneity in micrometer length and minute time scales. It is driven by actin polymerization, e.g., pushing the plasma membrane forward, and then regulated by a multitude of actin regulators. While mathematical modeling is central to system-level understandings of cell protrusion, most of the modeling is based on the ensemble average of actin regulator dynamics at the cellular or population levels, preventing from capturing the heterogeneous cellular activities. With these in mind, a systematic modeling framework is proposed in this paper for predicting velocities of heterogeneous protrusion of migrating cells driven by multiple molecular mechanisms. The modeling framework is developed through the integration of the multiple AutoRegressive eXogenous (ARX) models employing probability density input variables. Unlike conventional ARX models, it provides an effective framework for modeling heterogeneous subcellular behaviors with complex nonlinearities and uncertainties of dynamic systems. To train and validate the proposed model, numerous subcellular time series are extracted from time-lapse movies of migrating PtK1 cells using spinning disk confocal microscope: The current edge velocities and fluorescent intensities of mDia1, actin at the leading edge are used as the input while the future cell edge velocities are selected as an output. It is demonstrated that the proposed approach is highly effective in predicting the future trends of heterogeneous cell protrusion. In particular, by capturing the various multiple activities from the dataset, it is expected that it would improve the understanding of the molecular mechanism underlying cellular and subcellular heterogeneity.

The advent of smartphones has advanced the use of embedded sensors to acquire various physiological information. For example, smartphone camera sensors and accelerometers can provide heart rhythm signals to the subjects, while microphones can give respiratory signals. However, the acquired smartphone-based physiological signals are more vulnerable to motion and noise artifacts (MNAs) compared to using medical devices, since subjects need to hold the smartphone with proper contact to the smartphone camera and lens stably and tightly for a duration of time without any movement in the hand or finger. This results in more MNA than traditional methods, such as placing a finger inside a tightly enclosed pulse oximeter to get PPG signals, which provides stable contact between the sensor and the subject's finger. Moreover, a smartphone lens does not block ambient light in an effective way, while pulse oximeters are designed to block the ambient light effectively. In this paper, we propose a novel diversity method for smartphone signals that reduces the effect of MNAs during heart rhythm signal detection by 1) acquiring two heterogeneous signals from a color intensity signal and a fingertip movement signal, and 2) selecting the less MNA-corrupted signal of the two signals. The proposed method has advantages in that 1) diversity gain can be obtained from the two heterogeneous signals when one signal is clean while the other signal is corrupted, and 2) acquisition of the two heterogeneous signals does not double the acquisition procedure but maintains a single acquisition procedure, since two heterogeneous signals can be obtained from a single smartphone camera recording. In our diversity method, we propose to choose the better signal based on the signal quality indices (SQIs), i.e., standard deviation of instantaneous heart rate (STD-HR), root mean square of the successive differences of peak-to-peak time intervals (RMSSD-T), and standard deviation of peak values (STD-PV). As a performance metric evaluating the proposed diversity method, the ratio of usable period is considered. Experimental results show that our diversity method increases the usable period 19.53% and 6.25% compared to the color intensity or the fingertip movement signals only, respectively.

This paper proposes an exercise recommendation system for treating obesity that provides systematic recommendations for exercise and diet. Five body indices are considered as indicators for recommend exercise and diet. The system also informs users of prohibited foods using health data including blood pressure, blood sugar, and total cholesterol. To maximize the utility of the system, it displays recommendations for both indoor and outdoor activities. The system is equipped with multimode sensors, including a three-axis accelerometer, a laser, a pressure sensor, and a wrist-mounted sensor. To demonstrate the effectiveness of the system, field tests are carried out with three participants over 20 days, which show that the proposed system is effective in treating obesity.

This paper presents a new multi-input–multi-output (MIMO) fuzzy model for nonlinear system identification (SI) of smart structures under a variety of random forces. The fuzzy SI model is developed through the integration of wavelet transform (WT), multiple MIMO linear autoregressive exogenous (ARX) input models, Takagi–Sugeno (TS) fuzzy model, weighted linear least squares, and data clustering algorithms: MIMO WARX-TS fuzzy model. To demonstrate the effectiveness of the MIMO WARX-TS fuzzy model, a three-story building equipped with a magnetorheological (MR) damper under a variety of random signals is investigated. To train the proposed model, an artificial earthquake and control forces are used as input signals while displacement and acceleration responses are used as outputs. To validate the trained model, four real recorded earthquake signals are used. It is shown from the simulation that the proposed MIMO WARX-TS fuzzy identification algorithm is effective in estimating nonlinear behavior of a building–MR damper system under a variety of seismic excitations.

The application of smart control technology to both aging and new infrastructure is essential to extending service life, increasing life safety, and decreasing repair and replacement costs. One area of control technology research for civil engineering structures that has received little attention historically is that of high-impact loads, such as collision events. The dissipation of impact energy using smart control devices, such as magnetorheological (MR) dampers, leads to less plastic deformation and damage, and a lower likelihood of collapse in civil engineering structures. Due to the short duration and high variability in magnitude of potential impact loads, the issue of sub-optimal controller performance arises. In order to boost controller performance and improve the effectiveness of the control system, a radar-based impact load identifier is proposed. This radar-based impact load identifier will be used to estimate impact loads from imminent impacting objects, for example vessels and trucks, thus providing input information to the control system before the impact actually occurs. This paper presents the characterization and validation, through laboratory tests, of one part of the radar-based impact load identifier, the range and velocity estimation of the incoming moving objects. The range and velocity information are then used to direct structural control based on laboratory impact tests. An ultrawideband monostatic pulsed radar is used for range and velocity measurements of a laboratory-scale impacting vehicle. The range and velocity measurements obtained from the radar scans are verified using physical measurements and control testing. The tests showed great accuracy for both range and velocity with less than 3% error for each measurement and demonstrated structural control based on these measurements. It is shown from control system testing that the proposed approach is effective in reducing the structural impact responses by 11–30%, depending on the performance index, for pre-impact structural stiffening with passive control of MR dampers.

In this paper, a wavelet-filtered genetic-neuro-fuzzy (WGNF) control system design framework for response control of a highway bridge under various earthquake loads is discussed. The WGNF controller is developed by combining fuzzy logic, discrete wavelet transform, genetic algorithms, and neural networks for use as a control algorithm. To evaluate the performance of the WGNF algorithm, it is tested on a highway bridge equipped with hydraulic actuators. It controls the actuators installed on the abutments of the highway bridge structure. Various earthquakes used as input signals include an artificial earthquake, the El-Centro, Kobe, North Palm Springs, Turkey Bolu, Chi-Chi, and Northridge earthquakes. It is proved that the WGNF control system is effective in mitigating the vibration of the highway bridge under a variety of seismic excitation.

Recently, many mechanical components of vehicles are being replaced by electrical or electromechanical parts. This change leads to the reduced weight, higher fuel efficiency, and simpler diagnosis. In particular, a reliable and robust electromechanical brake control system has been focused. So, this article investigates that an application of brain limbic system–based control, a bio-inspired control strategy, to an electromechanical brake system is suitable for dynamic and uncertain driving conditions. To do this end, control parameters are optimized through genetic algorithm. Results show the effectiveness of the suggested control method in terms of control speed, reference tracking, and robustness to the disturbance.

This paper proposes a multiclass nonlinear relevance vector machine (MNRVM) model for health monitoring of smart structures equipped with magnetorheological (MR) dampers. The proposed model will be used to classify the damage statuses of the integrated structure-control systems subjected to ambient excitations. A numerical model of a three-story building equipped with an MR damper is studied to demonstrate the effectiveness of the proposed health monitoring schemes. Dynamic responses of the smart structures subjected to random excitations are measured. Discrete wavelet transform is applied to the obtained data to compress and filter noises of the measured data. As a next step, the compressed and de-noised signals are used for developing autoregressive (AR) models. Then the MNRVM is applied to the AR-coefficient data to classify them with respect to the damage statuses. As a baseline, the support vector machine (SVM) algorithm is considered. It is demonstrated that the proposed MNRVM framework is effective in classifying various damage statuses of the nonlinear smart structures subjected to ambient excitations. Simulation results also show that the MNRVM performs similar to the SVM with faster computation time.

In this article, a nonlinear multiclass support vector machine–based structural health monitoring system for smart structures is proposed. It is developed through the integration of a nonlinear multiclass support vector machine, discrete wavelet transforms, autoregressive models, and damage-sensitive features. The discrete wavelet transform is first applied to signals obtained from both healthy and damaged smart structures under random excitations, and it generates wavelet-filtered signal. It not only compresses lengthy data but also filters noise from the original data. Based on the wavelet-filtered signals, several wavelet-based autoregressive models are then constructed. Next, damage-sensitive features are extracted from the wavelet-based autoregressive coefficients and then the nonlinear multiclass support vector machine is trained by a variety of damage levels of wavelet-based autoregressive coefficient sets in an optimal method. The trained nonlinear multiclass support vector machine takes new test wavelet-based autoregressive coefficients that are not used in the training process and quantitatively estimates the damage levels. To demonstrate the effectiveness of the proposed nonlinear multiclass support vector machine, a three-story smart building equipped with a magnetorheological damper is studied. As a baseline, naive Bayes classifier–based structural health monitoring system is presented. It is shown from the simulation that the proposed nonlinear multiclass support vector machine–based approach is efficient and precise in quantitatively estimating damage statuses of the smart structures.

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

In this chapter, seismic vulnerability of smart structures is assessed using fragility analysis framework. The fragility analysis framework is effective to evaluate the performance and the vulnerability of structures under a variety of earthquake loads. To demonstrate the effectiveness of the seismic fragility analysis framework, a three-story steel frame building employing the nonlinear smart damping system is selected as a case study structure. To investigate the impact of sensor failures, various sensor damage case scenarios are considered. The seismic capacity of the smart building is determined based on the typical structural performance levels used in the literature. The unknown parameters for the seismic demand models are estimated using a Bayesian updating algorithm. Finally, the fragility curves of the smart structures under a variety of sensor damage cases are compared. It is proved from the extensive simulations that the proposed seismic fragility analysis framework is very effective in estimating the control performance of smart structures with sensor faults.

This chapter proposes a novel smart fuzzy control algorithm for mitigation of dynamic responses of seismically excited bridge structures equipped with control devices. The smart fuzzy controller is developed through the combination of discrete wavelet transform, backpropagation neural networks, and Takagi-Sugeno fuzzy model. To demonstrate the effectiveness of the proposed smart fuzzy controller, it is tested on a highway bridge equipped with magneto rheological (MR) dampers. It controls the smart dampers installed on the abutments of the highway bridge structure. The 1940 El-Centro and Kobe earthquakes are used as disturbance signals. It is demonstrated that the smart fuzzy controller is effective in reducing the structural responses of the highway bridge under a variety of seismic excitations.

Accurate heart rate detection is important in healthcare and exercise monitoring. Recently, heart rate monitoring using a smartphone has been highlighted due to its convenience and accuracy. In this paper, we hypothesize that our smartphone-based heart rate detection algorithm reliably detects heart rate based on fingertip image changes. Here, we have used successive video camera fingertip images with edge detection and smoothing techniques to process the fingertip images and to find out the heart rate of the subject. To investigate the capability of our proposed algorithm, we recruited 3 subjects and collected 2-min video data from each subject. We evaluated the performance of our proposed method by comparing it to the previous average intensity-based method [1]. Test results show that our proposed and previous methods give similar heart rate detection performance.

Sleep disorders are a common affliction for many people even though sleep is one of the most important factors in maintaining good physiological and emotional health. Numerous researchers have proposed various approaches to monitor sleep, such as polysomnography and actigraphy. However, such approaches are costly and often require overnight treatment in clinics. With this in mind, the research presented here has emerged from the question: “Can data be easily collected and analyzed without causing discomfort to patients?” Therefore, the aim of this study is to provide a novel monitoring system for quantifying sleep quality. The data acquisition system is equipped with multimodal sensors, including a three-axis accelerometer and a pressure sensor. To identify sleep quality based on measured data, a novel algorithm, which uses numerous physiological parameters, was proposed. Such parameters include non-REM sleep time, the number of apneic episodes, and sleep durations for dominant poses. To assess the effectiveness of the proposed system, three participants were enrolled in this experimental study for a duration of 20 days. From the experimental results, it can be seen that the proposed monitoring system is effective for quantifying sleep quality.

This paper proposes a wavelet-based fuzzy neuro control algorithm for the hazard mitigation of seismically excited buildings equipped with a hybrid control system. The wavelet-based fuzzy neuro controller is developed through the integration of discrete wavelet transform, artificial neural network, and a Takagi-Sugeno fuzzy controller. The hybrid control system is an integrated model of an actuator, a tuned mass damper, and viscous liquid dampers: an active tuned mass damper (ATMD) is located on the top floor of the structure and viscous liquid dampers are located on each floor. To demonstrate the effectiveness of the proposed wavelet-based adaptive neuro-fuzzy inference system (WANFIS) controller, an eight-story building employing passive viscous liquid dampers as well as an ATMD is investigated. A variety of earthquakes such as an artificial earthquake, the 1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes are used as disturbance signals. It is demonstrated that the WANFIS controller is effective in reducing the structural responses of the hybrid structure system subjected to a variety of disturbances.

This paper investigates the multi-objective optimization of active control systems for vibration control of three-dimensional (3D) high-rise buildings under a variety of earthquake excitations. To this end, a novel multi-objective genetic algorithm is developed through the integration of the best features of a non-dominated sorting II (NS2) genetic algorithm (GA) and an implicit redundant representation (IRR) GA. The proposed NS2-IRR GA finds not only minimum distributions of both actuators and sensors within structures, but also minimum dynamic responses of 3D structures. Linear quadratic Gaussian controllers, hydraulic actuators and accelerometers are used for implementation of active control systems within the 3D buildings. To demonstrate the effectiveness of the proposed NS2-IRR GA, two 3D building models are investigated using finite element methods, including low- and high-rise buildings. It is shown that the proposed NS2-IRR GA is effective in finding not only optimal locations and numbers of both actuators and sensors in 3D buildings, but also minimum responses of the 3D buildings. The simulation also shows that the control performances of the proposed approach significantly enhance those of the engineering judgment oriented benchmark layout, which is validated by comparisons of each performance using the same number of actuators.

This paper proposes a multi-input, multi-output (MIMO) nonlinear system identification (SI) of civil structures equipped with magnetorheological (MR) dampers. The nonlinear SI model is developed through the integration of multiple MIMO autoregressive exogenous (ARX) input models, Takagi-Sugeno (TS) fuzzy model, weighted linear least squares, fuzzy C-means and subtractive clustering algorithms. To demonstrate the effectiveness of the MIMO ARX-TS fuzzy model, a seismically excited three-story building equipped with an MR damper is investigated: Parameters of the premise and consequent parts for the proposed model are represented in detail and comparison between the original data and identified one is given. It is shown from the simulation that the proposed nonlinear MIMO ARX-TS fuzzy identification algorithm is effective in estimating nonlinear behavior of a seismically excited building-MR damper system.

This paper investigates the behavior of a seismically excited benchmark building employing magnetorheological dampers operated by a model-based fuzzy logic controller (MBFLC) formulated in terms of linear matrix inequalities (LMIs). The MBFLC is designed in a systematic way, while the traditional model-free fuzzy logic controller is designed via trial and error by experienced investigators. It is demonstrated from comparison of the uncontrolled and semiactive controlled responses that the proposed LMI-based MBFLC is effective in vibration reduction of a benchmark building under various earthquake loading conditions.

This paper proposes a linear matrix inequality (LMI)-based systematic design methodology for nonlinear control of building structures equipped with a magnetorheological (MR) damper. This approach considers stability performance as well as transient characteristics in a unified framework. First, multiple Lyapunov-based controllers are designed via LMIs such that global asymptotical stability of the building structure is guaranteed and the performance on transient responses is also satisfied. Such Lyapunov-based state feedback controllers are converted into output feedback regulators using a set of Kalman estimators. Then, these Lyapunov-based controllers and Kalman observers are integrated into a global nonlinear control system via fuzzy logic. To demonstrate the effectiveness of the proposed approach, a three-story building structure employing an MR damper is studied. The performance of the nonlinear control system is compared with that of a traditional linear optimal controller, i.e., H2/linear quadratic Gaussian (LQG), while the uncontrolled system response is used as the baseline. It is demonstrated from comparison of the uncontrolled and semiactive controlled responses that the proposed nonlinear control system design framework is effective in reducing the vibration of a seismically excited building structure equipped with an MR damper. Furthermore, the newly developed controller is more effective in mitigating responses of the structure than the H2/LQG controller.

This paper proposes nonlinear analyses of bridge pier-barge collision. A reinforced concrete bridge pier, a precast concrete deck and a Jumbo Hopper (JH) barge are modeled using LS-DYNA. The impact forces and structural responses of the reinforced concrete pier subjected to various barge impact loads are investigated. The American Association of State Highway and Transportation Officials (AASHTO) specifications and the previous studies on barge-pier collisions are used as baselines. It is demonstrated that the proposed finite element analysis (FEA) is effective to estimate the nonlinear behavior of the reinforced concrete bridge pier under barge impact loading.