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Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable‐stayed bridge using modal data from full‐scale dynamic testing.

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Applications of nanotechnology in the pavement industry have increased rapidly during the last decade in order to enhance a pavement’s sustainability and durability. Conventional asphalt binder generally does not provide sufficient resistance against rutting at high temperatures. Carbon black nano-particles (CBNPs, produced by perennial mountain trees’ carbonization) were mixed into the performance grade (PG) 58 asphalt binder in this study. Conventional asphalt binder tests (penetration, ductility and softening point), frequency sweep, performance grading, and bitumen bond strength tests were conducted to study the enhancement in the properties of asphalt binder. Dynamic modulus and wheel tracking tests were also performed to investigate the effect of CBNPs on asphalt mixture properties. Experimental results demonstrated that preferred dosage of CBNPs in asphalt is 10% by weight of the bitumen. Results of scanning electron microscopy (SEM) and storage stability tests validated homogenous and stable dispersion of CBNPs in the asphalt binder. Asphalt mixtures became stiffer and resistant to rutting at high temperatures by addition of CBNPs in asphalt binder. Significant improvement in bitumen aggregate bond strength was also observed by incorporating CBNPs. It is concluded that CBNPs can be used to effectively enhance the high-temperature performance and consequently the sustainability of flexible pavements.

Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.