This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries.

Structural digital models can be effectively established by spatially obtaining digital images using an unmanned aerial vehicle (UAV). One of the main purposes of the structural digital modeling is computer vision-based exterior damage detection of a target structure. To investigate micro-scale damage from the digital model, high-resolution digital images obtained with a close-up vision survey is typically required. However, serial image synthesis such as image stitching may cumulate stitching errors as the number of scanned images increases. Therefore, in this paper, a novel loop closure-based digital image stitching technique is proposed and experimentally validated using the close-up surveyed digital images acquired from an in-situ dam structure located in South Korea. The test results reveal that the proposed technique outperforms a non-loop closure-based image stitching technique, which can cause serious distortions, such as ghosting and vanishing phenomena.

It is difficult to calculate the output force of a single-layer piezoelectric actuator under voltage excitation. In this paper, the piezoelectric actuator is organically combined with the mass-spring-damping system, and the deformation of the piezoelectric actuator under voltage excitation is transformed into the displacement of the mass-spring-damping system. Then, according to the differential equation of the system, the formulae of the mechanical output of the piezoelectric actuator under sinusoidal alternating current and DC step excitation are obtained by using the Laplace change and the inverse change, respectively. Finally, the proposed equations are verified by using ceramic piezoelectric actuators and PVDF actuators, respectively. The results are compared with the existing ones, which shows that the proposed method is feasible, easy, and practical.

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

Infrastructure facilities contain various pipe systems, which can be considerably damaged by external loads such as earthquakes. Therefore, structural health monitoring (SHM) and safety assessment of pipes are crucial. Digital twin technology for SHM of pipes is important in the industry. This study proposes a digital twin system that estimates the behavior, stress, and external load of real-scale pipes in real time under simultaneous seismic and external loads using a minimum number of sensors. Vibration tests were performed to construct the digital twin system, and a numerical model was developed that considered the dynamic characteristics of a target pipe. Moreover, a reduced-order modeling technique of a numerical model was applied to enhance its real-time performance. The digital twin system successfully estimated the response of the pipe at all points. Verification of the digital twin system was performed by comparing it with the experimental parameters of a real-scale pipe. The proposed digital twin system can help enhance SHM and system's maintenance.