This article focuses on stability analysis and fuzzy controller synthesis for large neural network (NN) systems consisting of several interconnected subsystems represented by the NN model. Advanced and fuzzy NN differential inclusion (NNDI) for stability based on the developed algorithm with H infinity can be designed based on the evolved biological design. This representation is constructed using sector linearity for NN models. Sector linearity transforms a non-linear model into a linear model based on proposed operations. New sufficient conditions are realized in the form of LMI (linear matrix inequalities) to ensure the asymptotic stability of the trans-Lyapunov function. This transforms the nonlinear model into a linear model based on multiple rules. At last, a numerical case study with simulations is derived as illustration to prove its feasibility in real nonlinear structures.

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

Data-driven engineering is crucial for information delivery between design, fabrication, assembly, and maintenance of prefabricated structures. Design for manufacturing and assembly (DfMA) is a critical methodology for prefabricated bridge structures. In this study, a novel concept of digital engineering model that combined existing knowledge of DfMA with object-oriented parametric modeling technologies was developed. Three-dimensional (3D) geometry models and their data models for each phase of a construction project were defined for information delivery. Digital design models were used for conceptual design, including aesthetic consideration and possible variation during fabrication and assembly. The seismic performance of a bridge pier was evaluated by linking the design parameters to the calculated moment-curvature curves. Control parameters were selected to consider the tolerance control and revision of the digital models. Digitalized fabrication of the prefabricated members was realized using the digital fabrication model with G-code for a concrete printer or a robot. The fabrication error was evaluated and the design digital models were updated. The revised fabrication models were used in the preassembly simulation to guarantee constructability. For the maintenance of the bridge, the as-built information was defined for the prefabricated bridge piers. The results of this process revealed that data-driven information delivery is crucial for lifecycle management of prefabricated bridge piers.

Experimental and discrete element methods were used to investigate the effects of triple joints lengths and triple joint angle on the failure behavior of rock mass under uniaxial compressive test. Concrete samples with dimension of 20 cm × 20 cm × 5 cm were prepared. Within the specimen, three imbedded joint were provided. The joint lengths were 2 cm, 4cm and 6 cm. In constant joint lengths, the angle between middle joint and other joints were 30°, 60°, 90°, 120° and 150°. Totally 15 different models were tested under compression test. The axial load rate on the model was 0.05 mm/min. Concurrent with experimental tests, the models containing triple joints, length and joint angle are similar to the experiments, were numerical by Particle flow code in two dimensions (PFC2D). Loading rate in numerical modelling was 0.05 mm/min. Tensile strength of material was 1 MPa. The results show that the failure behaviors of rock samples containing triple joints were governed by both of the angle and the length of the triple joints. The uniaxial compressive strengths (UCS) of the specimens were related to the fracture pattern and failure mechanism of the discontinuities. Furthermore, it was shown that the compressive behavior of discontinuities is related to the number of the induced tensile cracks which are increased by decreasing the joint length. Along with the damage failure of the samples, the acoustic emission (AE) activities are excited. There were only a few AE hits in the initial stage of loading, then AE hits rapidly grow before the applied stress reached its peak. In addition, every stress drop was accompanied by a large number of AE hits. Finally, the failure pattern and failure strength are similar in both methods i.e., the experimental testing and the numerical simulation methods.

The deformation coordination between a rock/soil mass and an optical sensing cable is an important issue for accurate deformation monitoring. A stress-controlled triaxial apparatus was retrofitted by introducing an optical fiber into the soil specimen. High spatial resolution optical frequency domain reflectometry (OFDR) was used for monitoring the strain distribution along the axial direction of the specimen. The results were compared with those measured by a displacement meter. The strain measured by the optical sensing cable has a good linear relationship with the strain calculated by the displacement meter for different confining pressures, which indicates that distributed optical fiber sensing technology is feasible for soil deformation monitoring. The performance of deformation coordination between the sensing cable and the soil during unloading is higher than that during loading based on the strain transfer coefficients. Three hypothetical strain distributions of the triaxial specimen are proposed, based on which theoretical models of the strain transfer coefficients are established. It appears that the parabolic distribution of specimen strain should be more reasonable by comparison. Nevertheless, the strain transfer coefficients obtained by the theoretical models are higher than the measured coefficients. On this basis, a strain transfer model considering slippage at the interface of the sensing cable and the soil is discussed.

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

The negative stiffness of an active or semi-active damper system has been proven to be very effective in reducing dynamic response. Therefore, energy dissipation devices possessing negative stiffness, such as viscous inertial mass dampers (VIMDs), have drawn much attention recently. The control performance of the VIMD for cable vibration mitigation has already been demonstrated by many researchers. In this paper, a new optimal design procedure for VIMD parameters for taut cable vibration control is presented based on the fixed-points method originally developed for tuned mass damper design. A model consisting of a taut cable and a VIMD installed near a cable end is studied. The frequency response function (FRF) of the cable under a sinusoidal load distributed proportionally to the mode shape is derived. Then, the fixed-points method is applied to the FRF curves. The performance of a VIMD with the optimal parameters is subsequently evaluated through simulations. A taut cable model with a tuned VIMD is established for several cases of external excitation. The performance of VIMDs using the proposed optimal parameters is compared with that in the literature. The results show that cable vibration can be significantly reduced using the proposed optimal VIMD with a relatively small amount of damping. Multiple VIMDs are applied effectively to reduce the cable vibration with multi-modal components.

Modal parameters are widely used in bridge damage detection, finite element model (FEM) updating and design optimization. However, the conventional modal identification approaches require large number of sensors, enormous data processing workload, but normally result in mode shapes with low accuracy. This paper proposes a modal identification method of time-varying vehicle-bridge system using a single sensor. Firstly, the essential physical relationship between the instantaneous frequency of the vehicle-bridge system and the bridge mode shapes are derived. Subsequently, based on the synchroextracting transform, the instantaneous frequency of the system is tracked through the dynamic response collected by a single sensor, and further the modal parameters are estimated by using the derived physical relationship. Then numerical and experimental examples are conducted to examine the feasibility and effectiveness of the proposed method. Finally, the modal parameters identified by the proposed method are applied in bridge FEM updating. The results manifest that the proposed method identifies the modal parameters with high accuracy via a single sensor, and can provide reliable data for the FEM updating.