Semi-Active Control Systems in Bridge Engineering: A Review of the Current State of Practice

In view of the grave socioeconomic consequences of earthquake damage to bridge structures, along with their critical role in modern and older road and rail networks, this article attempts to identify and summarise the current trends in the use of semi-active control technology in bridge engineering, as an enhanced seismic response control solution, combining increased adaptability and reliability, compared to passive and active schemes. In this context, representative analytical and experimental studies, as well as some full-scale applications of semi-active control devices are first reviewed and a brief description of relevant benchmark studies is subsequently presented, with a view to serving as a point of reference for further research and development. A framework of performance-based control principles aiming at the aforementioned objectives is finally set forth

Seismic Behaviour of Cable-Stayed Bridges: A Review

Cable-stayed bridges represent backbones in the infrastructure networks and their adequate seismic response must be ensured. These structures present complex interactions between the deck, the cables, the towers and their foundation. This, in combination with the reduced damping and the outstanding slenderness of cable-stayed bridges, renders a unique dynamic response. A complete review on the state of knowledge about the seismic behaviour of cable-stayed bridges is presented here, with special attention to the analysis techniques. The current design trends in the seismic design and control of cable-stayed bridges are also presented

Artificial Intelligence in Civil Engineering

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering

Modeling and Fuzzy PDC Control and Its Application to an Oscillatory TLP Structure

An analytical solution is derived to describe the wave-induced flow field and surge motion of a deformable platform structure controlled with fuzzy controllers in an oceanic environment. In the controller design procedure, a parallel distributed compensation (PDC) scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. The Lyapunov method is used to carry out stability analysis of a real system structure. The corresponding boundary value problems are then incorporated into scattering and radiation problems. These are analytically solved, based on the separation of variables, to obtain a series of solutions showing the harmonic incident wave motion and surge motion. The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structural properties including platform width, thickness and mass can thus be drawn with a parametric approach. The wave-induced displacement of the surge motion is determined from these mathematical models. The vibration of the floating structure and mechanical motion caused by the wave force are also discussed analytically based on fuzzy logic theory and the mathematical framework to find the decay in amplitude of the surge motion in the tension leg platform (TLP) system. The expected effects of the damping in amplitude of the surge motion due to the control force on the structural response are obvious

Seismic Protection of Bridge Structures Using Shape Memory Alloy-Based Isolation Systems against Near-Field Earthquakes

The damaging effects of strong ground motions on highway bridges have revealed the limitations of conventional design methods and emphasized the need for innovative design concepts. Although seismic isolation systems have been proven to be an effective method of improving the response of bridges during earthquakes, the performance of base-isolated structures during near-field earthquakes has been questioned in recent years. Near-field earthquakes are characterized by long period and large- velocity pulses. They amplify seismic response of the isolation system since the period of these pulses usually coincides with the period of the isolated structures. This study explores the feasibility and effectiveness of shape memory alloy (SMA)-based isolation systems in order to mitigate the response of bridge structures against near-field ground motions. SMAs have several unique properties that can be exploited in seismic control applications. In this work, uniaxial tensile tests are conducted first to evaluate the degree to which the behavior of SMAs is affected by variations in loading rate and temperature. Then, a neuro-fuzzy model is developed to simulate the superelastic behavior of SMAs. The model is capable of capturing rate- and temperature-dependent material response while it remains simple enough to carry out numerical simulations. Next, parametric studies are conducted to investigate the effectiveness of two SMA-based isolation systems, namely superelastic-friction base isolator (S-FBI) system and SMA/rubber-based (SRB) isolation system. The S-FBI system combines superelastic SMAs with a flat steel-Teflon bearing, whereas the SRB isolation system combines SMAs with a laminated rubber bearing rather than a sliding bearing. Upon evaluating the optimum design parameters for both SMA-based isolation systems, nonlinear time history analyzes with energy balance assessment are conducted to compare their performances. The results show that the S-FBI system has more favorable properties than the SRB isolation system. Next, the performance of the S-FBI systems is compared with that of traditional isolation systems used in practice. In addition, the effect of outside temperature on the seismic response of the S-FBI system is assessed. It is revealed that the S-FBI system can successfully reduce the response of bridges against near-field earthquakes and has excellent re-centering ability

Adaptive Control to Reduce the Dynamic Response of Bridges Considering Parametric Changes

Parametric variations occur throughout bridges service life as a result of temperature fluctuations, cracking, localized damage and fatigue. Likewise, bridges parameters are difficult to estimate precisely; implemented control schemes may perform unsatisfactorily depending on their sensitivity to parametric changes. Adaptive control may present an alternative to control bridge structures, as adaptive schemes are able to calculate control gains that vary over time based on sensed responses. As a result, adaptive control strategies are able to sustain performance and deal with parametric variations. In this research, adaptive control schemes are developed and implemented to control bridges considering different types of structural configurations. The controllers’ ability in mitigating excessive seismic response and sustaining performance, the sensitivity of structural configuration and modeling considerations for control design and implementation on bridge structures are investigated. Initially, an adaptive control approach is developed to control two different highway bridges having as main control algorithm the simple adaptive control strategy. As a preliminary investigation, the control scheme is implemented and designed aiming to mitigate seismic responses of a three-span highway bridge considering realistic implementation and operation conditions. Following the initial investigation, a parametric study is conducted considering a two-span skewed highway bridge in order to assess the robustness of the control approach. Sequentially, adaptive semi-active control schemes are developed to control a cable-stayed bridge having as main control algorithms the simple adaptive control and the neuro-fuzzy control strategies. The bridge is subjected to parametric changes in order to assess the robustness of the control approaches. The effects of multi-support excitation with different angles of incidence are investigated. Lastly, earthquake records matched to the site’s design spectra effects are examined. The results indicate the adaptive schemes proposed in this research are a viable alternative to improve robustness to structural control of bridges. The developed adaptive control schemes are suitable to control large bridge structures, as they are able to reduce dynamic responses and offer robustness improvement when compared to nonadaptive schemes

Artificial Intelligence Approach for Seismic Control of Structures

Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties