Rademacher and Gaussian complexities: Risk bounds and structural results

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

Novel corrosion sensor based on carbon nanotube composites for structural health monitoring

Bridges are important infrastructures of all nations and are required for transportation of goods as well as human. A catastrophic failure can result in loss of lives and enormous financial hardship to the nation. Hence, there is an urgent need to monitor our infrastructures to prolong their life span, at the same time catering for heavier and faster moving traffics. Although various kinds of sensors are now available to monitor the health of the structures due to corrosion, they do not provide permanent and long term measurements. This paper investigates the fabrication of Carbon Nanotube (CNT) based composite sensors for structural health monitoring. The CNTs, a key material in nanotechnology has aroused great interest in the research community due to their remarkable mechanical, electrochemical, piezoresistive and other physical properties. Multi-wall CNT (MWCNT)/Nafion composite sensors were fabricated to evaluate their electrical properties when subjected to chemical solutions, to simulate a chemical reaction due to corrosion and real life corrosion experimental tests. The electrical resistance of the sensor electrode was dramatically changed due to corrosion. The novel sensor is expected to effectively detect corrosion in structures based on the measurement of electrical impedances of the CNT composite.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Structural Systematics of the Anhydrous 1:1 Proton-Transfer Compounds of 3,5-Dinitrosalicylic Acid with Aniline and Monosubstituted Anilines

The crystal structures of the proton-transfer compounds of 3,5-dinitrosalicylic acid (DNSA) with a series of aniline-type Lewis bases [aniline, 2-hydroxyaniline, 2-methoxyaniline, 3-methoxyaniline, 4-fluoroaniline, 4-chloroaniline and 2-aminoaniline] have been determined and their hydrogen-bonding systems analysed. All are anhydrous 1:1 salts: [(C6H8N)+(C7H3N2O7)-], (1), [(C6H8NO)+(C7H3N2O7)-], (2), [(C7H10NO)+(C7H3N2O7)-], (3), [(C7H10NO)+(C7H3N2O7)-], (4), [(C6H7FN)+(C7H3N2O7)-], (5), [(C6H7ClN)+(C7H3N2O7)-], (6), and [(C6H9N2)+(C7H3N2O7)-], (7) respectively. Crystals of 1 and 6 are triclinic, space group P-1 while the remainder are monoclinic with space group either P21/n (2, 4, 5 and 7) or P21 (3). Unit cell dimensions and contents are: for 1, a = 7.2027(17), b = 7.5699(17), c = 12.9615(16) Å, α = 84.464(14), β = 86.387(15), γ = 75.580(14)o, Z = 2; for 2, a = 7.407(3), b = 6.987(3), c = 27.653(11) Å, β = 94.906(7)o, Z = 4; for 3, a = 8.2816(18), b = 23.151(6), c = 3.9338(10), β = 95.255(19)o, Z = 2; for 4, a = 11.209(2), b = 8.7858(19), c = 15.171(3) Å, β = 93.717(4)o, Z = 4; for 5, a = 26.377(3), b = 10.1602(12), c = 5.1384(10) Å, β = 91.996(13)o, Z = 4; for 6, a = 11.217(3), b = 14.156(5), c = 4.860(3) Å, α = 99.10(4), β = 96.99(4), γ = 76.35(2)o, Z = 2; for 7, a = 12.830(4), b = 8.145(3), c = 14.302(4) Å, β = 102.631(6)o, Z = 4. In all compounds at least one primary linear intermolecular N+-H…O(carboxyl) hydrogen-bonding interaction is present which, together with secondary hydrogen bonding results in the formation of mostly two-dimensional network structures, exceptions being with compounds 4 and 5 (one-dimensional) and compound 6 (three-dimensional). In only two cases [compounds 1 and 4], are weak cation-anion or cation-cation π-π interactions found while weak aromatic C-H…O interactions are insignificant. The study shows that all compounds fit the previously formulated classification scheme for primary and secondary interactive modes for proton-transfer compounds of 3,5-dinitrosalicylic acid but there are some unusual variants.