An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings

Barontini, Alberto; Masciotta, Maria Giovanna; Ramos, Luís F.; Amado-Mendes, Paulo

doi:10.1016/j.proeng.2017.09.439

Cited by 30 publications

(12 citation statements)

References 26 publications

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“…It is the key factor that sets this review article from those that have been attempted in the past. Most of the previous reviews for OSP 50,62,118–120 have focussed only on optimization for vibration-based techniques without much discussion about the optimization algorithms used, while some others 105,121 only focus on the optimization algorithms without comprehensive discussion on the objectives for the optimization for SHM. This review article is aimed at informing the research community about the pitfalls and the opportunities of the different optimization tools available.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sensor placement for structural health monitoring: a review

Ostachowicz

Soman

Malinowski

2019

Structural Health Monitoring

236

154

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The deployment cost of the structural health monitoring (SHM) system is the major argument against the more widespread use of the structural health monitoring techniques. Optimization of sensor placement offers an opportunity to reduce the cost of the SHM system without compromising on the quality of the monitoring approach. Several studies in the area of optimization of sensor placement for SHM applications have been undertaken but the approach has been rather application specific. This article is an attempt to present an unbiased state of the art of the work carried out in the area. The article is targeted towards researchers working in the field of structural health monitoring and optimization of sensor placement as well as practising engineers. This article reviews the work in the area of optimization of sensor placement. It first presents the definition of the optimization problem and then describes each step of the optimization. The current state of the art is then classified based on the techniques for which the optimization of sensor placement has been optimized. The article covers vibration-based monitoring, strain monitoring and elastic wave-based monitoring, as in the eyes of the authors these three techniques are most commonly used and accepted in the SHM community. The article later discusses the different optimization algorithms that have been applied in the literature. The article highlights the different pitfalls of the optimization algorithms and the countermeasures different researchers have proposed to overcome the known shortcomings. In the later section, the multi-objective optimization or the problem definition, keeping in mind the structural as well as executional demands, is discussed. A section has also been developed to showcase the use of optimization of sensor placement techniques’ data fusion–based systems.

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Section: Introductionmentioning

confidence: 99%

Optimization of sensor placement for structural health monitoring: a review

Ostachowicz

Soman

Malinowski

2019

Structural Health Monitoring

236

154

View full text Add to dashboard Cite

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“…In this way, the consideration of second‐order effects in the optimization process allows approximating the funicular shape of a submerged arch taking only into account stable geometries. Note that the adoption in this problem of a genetic algorithm to obtain the optimum minima for the shape function is justified since such a function is highly nonlinear, following the approach of similar works in civil engineering (see examples on reviews elsewhere). The results here presented may be of interest for their application in the design of undersea installations involving arch‐type structures.…”

Section: Introductionmentioning

confidence: 99%

Buckling design of submerged arches via shape parameterization

Hernández-Díaz

Muñoz

Jiménez‐Alonso

et al. 2019

Comp and Math Methods

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Several of the geometric solutions obtained from the funicular design of submerged plane arches reach their critical buckling loads under service conditions. Consequently, the arch geometry must be modified, and then the bending moment increases considerably. Previous works have shown that the funicular shape of a submerged arch is close to either a parabolic or an elliptical form (depending on the water depth, the axial compressive force, and the arch height). In this work, such funicular shape is approximated using a parametric function of these two conical forms. Following this parameterization, the arch shape is optimized via an evolutionary algorithm taking into account its geometrically nonlinear behavior. As a result, the optimized shape shows a significative reduction in the second‐order bending stresses, due to a redistribution of the internal forces along the arch length. These results may find interesting applications in the optimal structural design of, among others, recreational underwater installations.

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“…Wireless sensor networks (WSNs) have a lot of applications around the world such as wireless body area networks [1], surveillance systems [2], intelligent transportation systems [3], and structural health monitoring (SHM). Structural health monitoring is applying damage detection for structures [4]. WSNs are becoming dominant in the SHM field due to their great sensing technologies and promising computing abilities [5].…”

Section: Introductionmentioning

confidence: 99%

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

El-Qawasma

Elfouly

Ahmed

2019

IET wirel. sens. syst.

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Nowadays, wireless sensor networks (WSNs) are considered an essential candidate to apply structural health monitoring (SHM). An important problem in this area is sensor placement optimization. In many research works, solving this problem focuses only on the network properties and requirements such as energy consumption, network coverage, …etc., without considering the civil engineering requirements. However, there are other research works that consider network and civil requirements while optimizing the sensor placement. Unfortunately, although minimizing the number of sensors is important, it has never been addressed. This could be noticed from the limited literature found that addresses this problem while considering both the civil and the network requirements. As a result, in this thesis we study the problem of minimizing the number of sensors for SHM in WSNs. The idea behind this research is to reduce the network size, which can solve some problems such as the scalability, installation time and cost. Our contribution in this work is not limited to the mathematical model of the mentioned problem, but will extend to solve the problem using different methods: the exhaustive search, genetic algorithm (GA), and a heuristic algorithm that applies the binary search. The problem is then solved for different number of sensors as well as different placements in many conducted experiments. Finally, the time complexity is evaluated to compare between all the applied methods. The obtained results showed that minimizing the number of sensors becomes more significant with big structures. Furthermore, the binary search algorithm is the best to use to solve the problem for small buildings. But, For larger buildings, there is a trade-off between the performance, and time complexity, where

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An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings

Cited by 30 publications

References 26 publications

Optimization of sensor placement for structural health monitoring: a review

Optimization of sensor placement for structural health monitoring: a review

Buckling design of submerged arches via shape parameterization

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

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