A comparison between different optimization criteria for tuned mass dampers design

Tuned mass sampers (TMDs) are widely used strategies for vibration control in many engineering applications, so that many TMD optimization criteria have been proposed till now. However, they normally consider only TMD stiffness and damping as design variables and assume that the tuned mass is a pre-selected value. In this work a more complete approach is proposed and then also TMD mass ratio is optimized. A standard single degree of freedom system is investigated to evaluate TMD protection efficiency in case of excitation at the support. More precisely, this model is used to develop two different optimizations criteria which minimize the main system displacement or the inertial acceleration. Different environmental conditions described by various char- acterizations of the input, here modelled by a stationary filtered stochastic process, are considered. Results show that all solutions obtained considering also the mass of the TMD as design variable are more efficient if compared with those obtained without it. However, in many cases these solutions are inappropriate because the optimal TMD mass is greater than real admissible values in practical technical applications for civil and mechanical engineering. Anyway, one can deduce that there are some interesting indications for applications in some actual contexts. In fact, the results show that there are some ranges of environmental parameters ranges where results attained by the displacement criterion are compatible with real applications requiring some percent of main system mass. Finally, the present research gives promising indications for complete TMD optimization application in emerging technical contexts, as micro- mechanical devices and nano resonant beam

Fractal Heterogeneous Media

A method is proposed for generating compact fractal disordered media, by generalizing the random midpoint displacement algorithm. The obtained structures are invasive stochastic fractals, with the Hurst exponent varying as a continuous parameter, as opposed to lacunar deterministic fractals, such as the Menger sponge. By employing the Detrending Moving Average algorithm [Phys. Rev. E 76, 056703 (2007)], the Hurst exponent of the generated structure can be subsequently checked. The fractality of such a structure is referred to a property defined over a three dimensional topology rather than to the topology itself. Consequently, in this framework, the Hurst exponent should be intended as an estimator of compactness rather than of roughness. Applications can be envisaged for simulating and quantifying complex systems characterized by self-similar heterogeneity across space. For example, exploitation areas range from the design and control of multifunctional self-assembled artificial nano and micro structures, to the analysis and modelling of complex pattern formation in biology, environmental sciences, geomorphological sciences, etc

Optimization of Stone Cutting Techniques for the Seismic Protection of Archaeological Sites

Since the beginning of civilization, history tells of the movement of art pieces, monuments and manufacts from site to site. The causes are multiple: the displacements due to the "spoils of war", ordered by kings and emperors, the movements caused by the need for reuse, especially in the early Christian period, and so forth. Considerations about the events of the past, yield a possible strategy to transform this concept into a technique for earthquake prevention of archaeological sites. The seismic safety retrofits have often proven to be scarcely effective, because of the difficulties involved in complex sites. The aim of this study is to analyze an "alternative" method of preventing natural disaster like floods, eruption and earthquakes, through the movimentation of the most representative structural elements of archaeological sites by decomposition of the masonry and marbles [1]. The procedure considers a process of "cutting optimization," calibrated on the characteristics of the specific material that has to be cut and then displaced in safer places (i.e., MEP, "manufact evacuation plan"). This process should not create excessive problems to the structure, and aims to reassembly the manufact in contexts able to guarantee safety through advanced earthquake-resistant expedients. From these considerations, the work develops a procedure to safeguard the archaeological site of Pompei (Naples), through an appropriate analysis of representative portions of the site, aimed to a careful handling and to a proper reconstruction in a safe location, from the seismic point of vie

Ductilityof fiber-reinforcedself-consolidatingconcreteundermulti-axial compression

The results of 12 multi-axial compression tests performed on cylinders made of self-consolidating concrete, plain (SCC) and reinforced with steel fibers (FR-SCC), are presented. In the experimental campaign, four ‘‘reference'' confining pressures (0, 1, 3 and 10 MPa) were applied on the lateral surface of the specimens. After the first stage of loading, when a hydraulic stress was applied to the cylinders, and progressively increased up to the value of a pre-established confining pressure, a longitudinal compressive load was used to generate crushing of concrete. During this failure, the post-peak behavior of SCC and FR-SCC can be defined by a non-dimensional function that relates the inelastic displacement and the relative stress during softening. Such a function also reveals the ductility of SCC, which increases with the confinement stress and with the fiber volume fraction. In particular, by adding 0.9% in volume of steel fibers, FR-SCC can show practically the same ductility measured in unreinforced SCC with 1MPa of confining pressure. Thus, the presence of an adequate amount of fibers in SCC columns is sufficient to create a sort of distributed confinement

Effects of Alternate Load Paths in damage evolution and identification in architectural heritage

The conservation of architectural heritage encompasses various aspects of technical sciences. In large buildings, made and further modified in different stages with different materials and techniques, it is difficult to measure the “health” of its structure in a simple way. The monitoring through displacement gauges represents one of the less costly solutions for estimating the evolution of damage in an existing structure: evidence of damage is represented by an increase of displacements. Anyway, due to the nonlinear behavior of large constructions, it is possible that displacements are measured only when the damage is at an advanced stage. In this sense, urgent measures have to be taken for ensure the stability of the construction. A preliminary study of the monitoring system and a good calibration of the threshold displacement values is required in order to limit the uncertainty about the true damage evolution stage. At the end, a full example illustrating the strategy to adopt in monitoring an historical construction is proposed

Mechonomics: design thinking for growth and resilience of sociotechnical organizations

In this paper the principal ideas of mechonomics are introduced. Mechonomics is a neologism indicating the possibility of predicting the behaviour of sociotechnical organizations in the complex and interconnected world of the 21st century by means of models borrowed from structural mechanics. In particular, the concepts of growth, resilience and robustness of the organizations are discussed. The analogy with structural and natural systems is shown to be sound and permits to interpret the effects of the size of the organization and of its internal arrangement and collapse of enterprises and institutions. Consequence-based design, as the tool able to tackle with unpredictable stimuli and external effects, is introduced as the only robust philosophy that should pervade design and management of sociotechnical organizations

Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring

Innovative strategies to preserve the Italian engineering heritage: the historical tunnels.

Italy, a nerve center for Western culture, holds the largest number of artistic and cultural assets declared World Heritage by UNESCO. From the Romans to the present day, an ever-growing infrastructure system, rich in tunnels, bridges and viaducts, has been the expression of a high engineering expertise. For the management of the aforementioned complex infrastructure heritage, the development of automated control and maintenance plans is one of the issues on which the engineering and research community focuses its resources and efforts. In this study, an approach is proposed to automate the process of classifying defects in tunnels using deep learning techniques to protect and maintain the concrete tunnel lining. The acquisition of images from non-destructive monitoring techniques, such as Ground Penetrating Radar, within a supervised learning process allows the creation of an effective tool for the automatic detection of severe defects such as cracks, anomalies, and voids. The obtained results provided for a high degree of accuracy in identifying the tunnels’ structural condition. The use of the developed strategy, based on machine learning and non-invasive inspection techniques, is costeffective for infrastructure managers. Such a procedure reduces both the number of invasive interventions on the tunnel lining and the time and cost associated with employing specialized technicians