An integrated control system with smart structure is proposed for active structural acoustic control (ASAC). It is mainly used to integrate the advantages of centralized and decentralized ASAC. Each smart structure contains a relatively independent controller, which forms a distributed control. The coordination and cooperation between smart structures is mainly realized by sending control factors (secondary generalized modal force) from the upper coordination unit (coordination structure) to each smart structure. The control factor can reflect the weight of each smart structure on vibration noise control, and play a key role in noise control. The control factors are extracted from the blend function in the bottom control units (smart structures) and stored in the coordination structure. This design method ensures the consistency of the internal functions of each smart structure and lays a foundation for decentralized control. In addition, whether the control factor is allocated to the smart structure depends on the real-time changes of the sound field. Through the intelligent allocation of the control factors, the global (centralized) control is realized and the coupling problem between smart structures is solved. Since the control system does not have a centralized controller, it appears as a decentralized control in form; at the same time, the centralized control in algorithm is achieved by extracting and redistributing the control factors. Therefore, the control system integrates the advantages of decentralized and centralized control.

This study presents a damage detection method based on modal responses for building structures using convolutional neural networks (CNNs). The modal responses used in the method are obtained from the dynamic responses, which are measured in a building structure under ambient excitations; these are then transformed to a modal participation ratio (MPR) value for a measuring point and mode. As modal responses vary after damages in the structures, the MPR for a specific location and mode also changes. Thus, in this study, MPR variations, which can be obtained by comparing the MPRs of damaged and healthy structures, are utilized for damage detection without the need for identification of modal parameters. Since MPRs are derived for the number of measuring points (N) in the structure as well as the same number of modes (N), the MPRs and MPR variations can be arranged as an N × N matrix. This low-dimensional MPR variations set is used as the input map of the presented CNN architecture and information about damage locations and severities of the target structure is set as the output of the CNN. The presented CNN is trained for establishing the relationship between MPR variations and damage information and utilized to estimate the damage. The presented damage detection method is applied to numerical examples for two multiple degrees of freedoms and a three-dimensional ASCE benchmark numerical model. Training datasets created from damage scenarios assuming changes in the stiffness are used to train the CNN and the performance of this CNN is verified. Finally, this study examines how variations in the operator size and number of layers in the CNN architecture affect the damage detection performance of CNNs.

This paper has proposed an intelligent Evolutionary Bat Algorithm (afterward, EBA) Fuzzy NN (Neural Network) controller used to ensure the asymptotic simulation stability of a mathematics nonlinear system for a smart structure. The smart evolutionary fuzzy NN model adopts an NN numerical model and the linear differential inclusion (LDI) concept. Denotation of the nonlinear dynamics is constructed by transforming the nonlinear model into a multi-rule-based sector nonlinear form of mathematics linear numerical models, and implementing a new sufficient mathematics condition whereby the asymptotic simulation stability of the intelligent structure is guaranteed by the Lyapunov mathematics function, linear matrix inequality (LMI). The high frequency is also injected as an auxiliary to stabilize these nonlinear systems. According to the relaxed method injected with dithered auxiliary, the nonlinear system can be guaranteed stable by appropriately regulating the parameters. Finally, there is a numerical resultant example with simulation results which is designated in order to precisely demonstrate the advantages of the smart intelligent controller and the proposed control scheme compared to previous schemes.

This paper presents a first proposed real-sized Hidden boundary Void precast concrete Slab, a so-called HVS. In order to assess and investigate push-over behaviors of the novel structural system, five types of real size specimens manufactured in factory are tested by corresponding static loading protocol experiments. Bending tests along with shear tests in both cases of presence and absence of topping concrete slabs are performed. Besides, composite slabs, which are two half-unit products connected by steel plates and U-bolts in the long edges, are surveyed to assess uniform characteristics of slab systems. Simultaneously, estimating calculations are proposed for the structural bearing capacity of partially prestressed concrete flagged sections. Proposed equations are developed according to provisions of several current global and local design standards. Moreover, this study provides another predicting approach using finite element analysis of MIDAS FEA for analytical performances of specimens. Thanks to these experimental and analytical results, the general characteristic of HVS may be observed, and then studied for realization in the field of prestressed precast concrete industries for construction.

This paper used a magnetic flux leakage (MFL) method compatible with steel structures to analyze quantitative change in a leakage signal due to defects on the surface of a railroad. A numerical simulation using a two-dimensional finite element method (2D-FEM) was used to analyze MFL signals from defects on the railroad. An experiment was then carried out to investigate the capability of the MFL-based non-destructive evaluation (NDE). We also focused on the velocity effect of the MFL signals by analyzing the magnetic hysteresis phenomenon. The quantitative change in leakage signals was determined by selecting depth of the defect and inspection velocity as parameters in a simulation and in an experiment. The MFL signals obtained showed variations that were simultaneously affected by inspection velocity and defect depth. MFL-based damage detection in a railroad is conclusively confirmed to be sufficiently feasible within the range of operational speeds of an inspection train.

The NRCS-CN model is one of the commonly used models for the estimation of discharge peak in a watershed in Saudi Arabia using the DEM outputs and daily rainfall data. The current study aims to classify the sub-basins using rainfall volume, rain off volume and estimate the discharge peak using NRCS-CN method. The drainage map of the studied sub-basin areas was extracted from SRTM Dem 30 m outputs. To estimate the surface runoff and its discharge peak, the drainage area was divided into four tributary basins for Wadi Al-Aqiq using the WMS software outputs from SRTM Dem 30 m. The extracted sub-basins were Wadis Rim, Al Yitimah, Al Ishsh and Sha'ib Ruwawah. The results indicate the importance of the integrated use of the digital processing outputs of the DEM in extracting the morphometric variables necessary in calculating the concentration-time, unit peak discharge and the land resources map in determining the runoff curve. In addition, the weighted curve numbers were ranged between 88.4, 89.9, and 92.1 in Wadi Al Ishsh, wadi Rim, and wadi Al Aqiq watersheds, respectively. It was evident that the attained results can be calibrated by the actual recorded discharge and related to several watersheds.

To avoid the issues of incomplete strain transfer ratio and insufficient bonding strength of a monolithic stressed antenna, this paper presents an unstressed deformation sensor based on two-layer patch antenna for structural health monitoring. The proposed sensor is composed of a monolithic patch antenna and a stacked patch generating two fundamental resonant frequencies within a 3-to-7 GHz band. The resonant frequencies' shifts caused by the offset of the stacked patch were selected as the sensing parameters. An equivalent circuit was used to analyze the sensing method, which shows the relative displacement to be linear to the shift of resonant frequencies. This phenomenon was then checked by numerical simulation using the Ansoft High Frequency Structure Simulator 15 (HFSS15) and experiments in laboratory using both wired and wireless setups. Furthermore, the accuracy of measurement is verified to be increased by combining two resonant frequencies.

Power consumption has become the key constraint in electronics design, since the MOSFET threshold and hence the supply voltage can no longer be scaled. This trend calls for new device concepts such as Spintronic devices that are fundamentally different from CMOS. A carbon nanotube field-effect transistor (CNTFET) refers to a field-effect transistor that utilizes a single carbon nanotube or an array of carbon nanotubes as the channel material instead of bulk silicon in the traditional MOSFET structure. Magnetic tunnel junction (MTJ) is an emerging technology which has many advantages when used in logic in memory structures in conjunction with CMOS. In this paper, we present novel designs of hybrid CNTFET-MTJ circuits; AND, XOR and 1-bit full adder. The proposed CNTFET-MTJ full adder design has 20 times lower Power-delay-product (PDP) compared to the previous CMOS- MTJ full adder. Also, the delay in CNTFET-MTJ circuit is reduced 20 times compared to the CMOS- MTJ circuit.

Vibration characteristics of actuated Shape Memory Alloy (SMA) embedded smart composite beam are studied and it is extended to reduce the impact of resonance in cantilever beams. Smart composite beam with SMA embedded at neutral layer is studied for its vibrational characteristics under martensite and austenite conditions. The smart beam is developed as a analytical model incorporating the change in Young's modulus and damping factor under martensite and austenite conditions of SMA. The variation of natural frequency and damping are evaluated and verified with experimentation at different volume fraction of SMA. The thermo elastic nature of SMA and GFRP incorporated in the numerical model depicts the shift in natural frequency of 10% and reduction in magnification factor of 50% under actuation conditions. The frequency response of the smart beam depicts the capability of SMA in active vibration control and improvement of the structural health of composite beam. The thermo mechanical analytical model derived can be utilized to optimize the volume fraction of SMA to be embedded. The study can be extended to optimize actuation current to minimize the effect of resonance.

In this study, the effect of agitation speed as a key process parameter on the morphology and particle size of epoxy-Poly (methyl methacrylate) (PMMA) microcapsules was investigated. Thus, a new interpretation is presented to relate between the microcapsule size to rotational speed so as to predict the particle size at different agitation speeds from the initial capsule size. The PMMA shell capsules containing EC 157 epoxy and hardener as healing materials were fabricated through the internal phase separation method. The process was performed at 600 and 1000 rpm mechanical mixing rates. Scanning electron microscopy (SEM) revealed the formation of spherical microcapsules with smooth surfaces. According to static light scattering (SLS) results, the average diameter size of the epoxy/PMMA capsules at two mixing rates were 7.49 and 5.11 ㎛ for 600 and 1000 rpm, respectively, indicating that the mean size increased as the mixing rates of the process increased. The D₅₀, D₉₀ and mean particle size values were the lowest for hardener/PMMA microcapsules at 1000 rpm. Moreover, the Fourier transform infrared (FTIR) spectroscopy was conducted to describe the chemical structure of epoxy and hardener PMMA capsules. To investigate the reinforcing role of microcapsules, they embedded in EPL-1012 epoxy resin with various amounts of 1 and 2.5 wt.% epoxy/PMMA capsules. The investigation also involved the effect of microcapsules on mechanical behavior as well as the reinforcement of polymer composite material. Experimental results showed that the tensile strength of the self-healing polymer composite slightly increased by 1 wt.% PMMA microcapsules prepared at 1000 rpm and then reduced with an increase in the concentration and mean size diameter of PMMA microcapsules. In addition, a similar trend of Young's modulus was seen for pristine epoxy matrix and microcapsule-loaded epoxy composite.

With the same horizontal acceleration control performance, the horizontal displacement control performances of sloped rolling-type seismic isolators passively provided with stepwise variable parameters, as well as constant ones, are numerically investigated in this study. The first design possesses a smaller sloping angle with larger damping force at smaller horizontal isolation displacement and a larger sloping angle with smaller damping force at larger horizontal isolation displacement. In other words, this design has stepwise increased sloping angles and stepwise decreased damping force with increasing horizontal isolation displacement. The second design has an opposite design philosophy to the first one, i.e., it has stepwise decreased sloping angles and stepwise increased damping force with increasing horizontal isolation displacement. A series of numerical results present that for sloped rolling-type seismic isolators designed with a constant sloping angle and damping force, in general, the larger the damping force (in other words, the smaller the sloping angle), the smaller and the larger the horizontal maximum and residual displacement responses presented, respectively. The first and second designs with stepwise variable parameters each have its advantage for suppressing horizontal isolation displacement under far-field and pulse-like near-fault ground motions because of their larger energy dissipation capabilities designed at different stages. When the horizontal isolation displacement responses at the end of ground motions are still within the first slope rolling range with a larger sloping angle of the second design, as expected, adopting the second design can exhibit a better re-centering performance than adopting the first design. To have acceptable displacement control performances and without scarifying acceleration control performances under diverse seismic demands, compared with adopting the designs with constant parameters and the first design, adopting the second design could be an alternative solution and better choice.

Implementing unmanned aerial vehicles (UAVs) on concrete surface-crack inspection leads to a promising visual crack detection approach. One of the challenges for automated field visual cracking inspection is image degradation caused by the rain or fog and motion blur during data acquisition. The present study combines two deep neural networks to address the image degradation problem. By using the Variance of Laplacian algorithm for quantifying image clarity, the proposed deep neural networks can remarkably enhance the sharpness of the degraded images. After vision enhancement process, Mask Region Convolutional Neutral Network (Mask R-CNN) was developed to perform automated crack identification and segmentation. Results show a 8~13% enhancement in prediction accuracy compared to the degraded images, indicating that the proposed deep learning-based vision enhancement method can effectivey identify and segment concrete surface cracks from photos captured by UAVs.

The principles of productive active and semi-active civil and infrastructure engineering structural control date back 40 years and significant progress has been recorded in those four decades. Smart structures typically have some control systems that enable them to deal with perturbations. The active vibration management techniques have been applied numerically and experimentally in order to reduce the vibrational levels of lightweight economic composite structures. Smart composite beams and plates have been produced and tested with surface-based piezoelectric sensors and actuators. It has been found that an effective model of smart composite plates can predict the dynamic characteristics. Utilizing Genetic Algorithm (GA) was designed and implemented. Two regression model as root mean square (RMSE) and determination coefficient (R²) were used. The first and second bending modes are operated effectively by a beam, and simultaneous vibration levels are significantly reduced for the conductive plates by the simultaneous operation of the bending and twisting modes. Vibration management is realized by using efficient control. GA could show better performance for managing linear feedback laws under given assumptions.