A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

Failure patterns of rock specimens represent valuable information about the mechanical properties and crack evolution mechanism of rock. Several kinds of research have been conducted regarding the failure mechanism of brittle material, however; the influence of brittleness on the failure mechanism of rock specimens has not been precisely considered. In the present study, experimental and numerical examinations have been made to evaluate the physical and mechanical phenomena associated with rock failure mechanisms through the uniaxial compression test. In the experimental part, Unconfined Compressive Strength (UCS) tests equipped with Acoustic Emission (AE) have been conducted on rock samples with three different brittleness. Then, the numerical models have been calibrated based on experimental test results for further investigation and comparing the micro-cracking process in experimental and numerical models. It can be perceived that the failure mode of specimens with high brittleness is tensile axial splitting, based on the experimental evidence of rock specimens with different brittleness. Also, the crack growth mechanism of the rock specimens with various brittleness using discrete element modeling in the numerical part suggested that the specimens with more brittleness contain more tensile fracture during the loading sequences.

The optimum damping and tuning frequency ratio of the tuned mass damper-inerter (TMDI) for the base-isolated structure is obtained using the numerical searching technique under stationary white-noise and filtered white-noise earthquake excitation. The minimization of the isolated structure's mean-square relative displacement and absolute acceleration, as well as the maximization of the energy dissipation index, were chosen as the criteria for optimality. Using a curve-fitting technique, explicit formulae for TMDI damping and tuning frequency for white-noise excitation are then derived. The proposed empirical expressions for TMDI parameters are found to have a negligible error, making them useful for the effective design of base-isolated structures. The effectiveness of TMDI and its optimum parameters are influenced by the soil condition and isolation frequency, according to the comparison made of the optimized parameters and response with different soil profiles. The effectiveness of an optimally designed TMDI in controlling the displacement and acceleration response of the flexible isolated structure under real and pulse-type earthquakes is also observed and found to be increased as the inertance mass ratio increases.

Heritage timber structures represent the history and culture of a nation. These structures have been inherited from previous generations; however, they inevitably exhibit deterioration over time, potentially leading to structural deficiencies. Structural Health Monitoring (SHM) offers the potential to assess operational anomalies, deterioration, and damage through processing and analysis of data collected from transducers and sensors mounted on the structure. This paper reports on the design and implementation of a long-term SHM system on the Feiyun Wooden Pavilion in China, a three-story timber building built more than 500 years ago. The principles and features of the design and implementation of SHM systems for heritage timber buildings are systematically discussed. In total, 104 sensors of 6 different types are deployed on the structure to monitor the environmental effects and structural responses, including air temperature and humidity, wind speed and direction, structural temperatures, strain, inclination, and acceleration. In addition, integrated data acquisition and transmission subsystem using a newly developed software platform are implemented. Selected preliminary statistical and correlation analysis using one year of monitoring data are presented to demonstrate the condition assessment capability of the system based on the monitoring data.

Oil refineries' Fluid Catalytic Cracking Units (FCCU) when in full operation may exhibit strong fluid dynamics caused by turbulent flow in the piping system that may induce vibrations in other mechanical and structural components of the Unity. This paper reports on the experimental-theoretical-computational program performed to get the vibration properties and the dynamic response amplitudes to find out alternative solutions to attenuate the excessive vibrations that were causing fatigue fractures in components of the bottle like reactor-regenerator of an FCC unit in operation in an existing oil refinery in Brazil. Solutions to the vibration problem were sought with the aid of a 3D finite element model calibrated with the results obtained from experimental measurements. A short description of the found solutions is given and their effectiveness are shown by means of numerical results. The solutions were guided by the concepts of structural stiffening and dynamic control performed by a nonlinear pendulum controller whose mechanical design was based on parameters determined by means of a parametric study carried out with 2D and 3D mathematical models of the coupled pendulum-structure system. The effectiveness of the proposed solutions is evaluated in terms of the fatigue life of critical welded connections.

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

High-rise structures prone to large vibrations under the action of strong winds, resulting in fatigue damage of the structural components and the foundation. A novel compound damping cable system (CDCS) is proposed to suppress the excessive vibrations. CDCS uses tailored double cable system with increased tensile stiffness as the connecting device, and makes use of the relative motion between the high-rise structure and the ground to drive the damper to move back-and-forth, dissipating the vibration mechanical energy of the high-rise structure so as to decaying the excessive vibration. Firstly, a third-order differential equation for the free vibration of high-rise structure with CDCS is established, and its closed form solution is obtained by the root formulas of cubic equation (Shengjin's formulas). Secondly, the analytical solution is validated by a laboratory model experiment. Thirdly, parametric analysis is conducted to investigate how the parameters affect the vibration control performance. Finally, the dynamic responses of the high-rise structure with CDCS under harmonic and stochastic excitations are calculated and its vibration mitigation performance is further evaluated. The results show that the CDCS can provide a large equivalent additional damping ratio for the vibrating structures, thus suppressing the excessive vibration effectively. It is anticipated that the CDCS can be used as a good alternative energy dissipation system for vibration control of high-rise structures.