Guang-Dong Zhou

Sensor Placement Optimization in Structural Health Monitoring Using Cluster-In-Cluster Firefly Algorithm

The determination of the optimal sensor placement (OSP) is a significant task that must be completed before a structural health monitoring (SHM) system is implemented on a real structure. The firefly algorithm (FA) is a recently developed nature-inspired metaheuristic algorithm for continuous optimization problems. This paper proposes a cluster-in-cluster firefly algorithm (CiCFA) for the optimum SHM sensor deployment. First, the code defining position coordinates in basic FA is replaced with a one-dimensional binary coding system, and the Euclidean distance is replaced by the Hamming distance. Then, a movement scheme for a darker firefly approaching a lighter firefly is developed. Last, a cluster-in-cluster strategy is employed to improve the convergence speed. In addition, a self-adaptive dynamic penalty function is introduced to convert the constraint imposed by the limited wireless data transmission range in the optimal wireless sensor placement (OWSP) problem to an unconstrained optimization problem. To demonstrate the effectiveness and applicability of the proposed optimization method, numerical examples of wired sensor placement and wireless sensor placement are carried out. The results reveal that the CiCFA provides a better sensor configuration than the genetic algorithm with high efficiency and high stability and can be applied in the OWSP problem. The proposed CiCFA provides a promising alternative for solving the OSP problem in practical SHM systems.

Wireless Sensor Placement for Bridge Health Monitoring Using a Generalized Genetic Algorithm

The optimal placement of wireless sensors is very different from conventional wired sensor placement due to the limited transmission range of the wireless sensors. This constraint on the inter-sensor distance makes the optimization problem difficult to solve with conventional gradient-based methods. In this paper, an improved generalized genetic algorithm (GGA) based on a self-adaptive dynamic penalty function (SADPF) is proposed for the optimal wireless sensor placement (OWSP) in bridge vibration monitoring. The mathematical model of the OWSP problem is established, and it considers both the bridge vibration monitoring requirements and the constraints of the data transmission range in wireless sensor networks (WSNs). SADPF, which can automatically adjust the amount of penalization for constraint violations according to the evolution generation number and the degree of violation, is then developed so that the wireless sensor placement can be optimized using GGA. Subsequently, the GGA is improved by implementing an elite conservation strategy, a worst elimination policy and a dual-structure coding system. Finally, a numerical experiment is presented with a long-span suspension bridge to demonstrate the feasibility and efficiency of the proposed method, and some indispensible discussions are also given. The results indicate that the wireless sensor configurations that are optimized by the improved SADPF-based GGA can simultaneously meet the data transmission demands in a WSN and fulfill the requirements for structural condition assessment. The developed SADPF can minimize the influence of the limited data transmission range on the search process for the OWSP. The improved SADPF-based GGA quickly and robustly converges to the global optimal solution.

A generalized Pareto distribution–based extreme value model of thermal gradients in a long-span bridge combining parameter updating:

Estimating extreme value models with high reliability for thermal gradients is a significant task that must be completed before reasonable thermal loads and possible thermal stress in long-span bridges are evaluated. In this article, a generalized Pareto distribution–based extreme value model combining parameter updating has been developed to describe the statistical characteristics of thermal gradients in a long-span bridge. The procedure of excluding correlation and the approach of selecting a proper threshold are suggested to prepare samples for generalized Pareto distribution estimation. A Bayesian estimation, which has the capability of updating model parameters by fusing prior information and incoming monitoring data, is proposed to fit the generalized Pareto distribution–based model. Furthermore, the Gibbs sampling, which is a Markov chain Monte Carlo algorithm, is adopted to derive the Bayesian posterior distribution. Finally, the proposed method is applied to the field monitoring data of thermal gradients in the Jiubao Bridge. The extreme value models of thermal gradients for the Jiubao Bridge are established, and the extreme thermal gradients with different return periods are extrapolated. The results indicate that the generalized Pareto distribution–based extreme value model has a strong ability to represent the statistical features of thermal gradients for the Jiubao Bridge, and the Bayesian estimation combining parameter updating provides high-precision generalized Pareto distribution–based models for predicting extreme thermal gradients. The predicted extreme thermal gradients are expected to evaluate and design long-span bridges.

A Comparative Study of Genetic and Firefly Algorithms for Sensor Placement in Structural Health Monitoring

Optimal sensor placement (OSP) is an important task during the implementation of sophisticated structural health monitoring (SHM) systems for large-scale structures. In this paper, a comparative study between the genetic algorithm (GA) and the firefly algorithm (FA) in solving the OSP problem is conducted. To overcome the drawback related to the inapplicability of the FA in optimization problems with discrete variables, some improvements are proposed, including the one-dimensional binary coding system, the Hamming distance between any two fireflies, and the semioriented movement scheme; also, a simple discrete firefly algorithm (SDFA) is developed. The capabilities of the SDFA and the GA in finding the optimal sensor locations are evaluated using two disparate objective functions in a numerical example with a long-span benchmark cable-stayed bridge. The results show that the developed SDFA can find the optimal sensor configuration with high reliability. The comparative study indicates that the SDFA outperforms the GA in terms of algorithm complexity, computational efficiency, and result quality. The optimization mechanism of the FA has the potential to be extended to a wide range of optimization problems.

Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm

Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed.

A Whole-Range S–N Curve for Fatigue Assessment of Steel Orthotropic Bridge Decks

The fatigue assessment of orthotropic bridge decks under routine traffic loading is a significant task to ensure the serviceability and safety of steel bridges. The sequential law computes fatigue ...