Safety and reliability are prime concerns in aircraft performance due to the involved costs and risk to lives. Despite the best efforts in design methodology, quality evaluation in production and structural integrity assessment in-service, attainment of one hundred percent safety through development and use of a suitable in-flight health monitoring system is still a farfetched goal. And, evolution of such a system requires, first, identification of an appropriate Technique and next its adoption to meet the challenges posed by newer materials (advanced composites), complex structures and the flight environment. In fact, a quick survey of the available Non-Destructive Evaluation (NDE) techniques suggests Acoustic Emission (AE) as the only available method. High merit in itself could be a weakness - Noise is the worst enemy of AE. So, while difficulties are posed due to the insufficient understanding of the basic behavior of composites, growth and interaction of defects and damage under a specified load condition, high in-flight noise further complicates the issue making the developmental task apparently formidable and challenging. Development of an in-flight monitoring system based on AE to function as an early warning system needs addressing three aspects, viz., the first, discrimination of AE signals from noise data, the second, extraction of required information from AE signals for identification of sources (source characterization) and quantification of its growth, and the third, automation of the entire process. And, a quick assessment of the aspects involved suggests that Artificial Neural Networks (ANN) are ideally suited for solving such a complex problem. A review of the available open literature while indicates a number of investigations carried out using noise elimination and source characterization methods such as frequency filtering and statistical pattern recognition but shows only sporadic attempts using ANN. This may probably be due to the complex nature of the problem involving investigation of a large number of influencing parameters, amount of effort and time to be invested, and facilities required and multi-disciplinary nature of the problem. Hence as stated in the foregoing, the need for such a study cannot be over emphasized. Thus, this thesis is an attempt addressing the issue of analysis and automation of complex sets of AE data such as AE signals mixed with in-flight noise thus forming the first step towards in-flight monitoring using AE. An ANN can in fact replace the traditional algorithmic approaches used in the past. ANN in general are model free estimators and derive their computational efficiency due to large connectivity, massive parallelism, non-linear analog response and learning capabilities. They are better suited than the conventional methods (statistical pattern recognition methods) due to their characteristics such as classification, pattern matching, learning, generalization, fault tolerance and distributed memory and their ability to process unstructured data sets which may be carrying incomplete information at times and hence chosen as the tool. Further, in the current context, the set of investigations undertaken were in the absence of sufficient a priori information and hence clustering of signals generated by AE sources through self-organizing maps is more appropriate. Thus, in the investigations carried out under the scope of this thesis, at first a hybrid network named "NAEDA" (Neural network for Acoustic Emission Data Analysis) using Kohonen self-organizing feature map (KSOM) and multi-layer perceptron (MLP) that learns on back propagation learning rule was specifically developed with innovative data processing techniques built into the network. However, for accurate pattern recognition, multi-layer back propagation NN needed to be trained with source and noise clusters as input data. Thus, in addition to optimizing the network architecture and training parameters, preprocessing of input data to the network and multi-class clustering and classification proved to be the corner stones in obtaining excellent identification accuracy. Next, in-flight noise environment of an aircraft was generated off line through carefully designed simulation experiments carried out in the laboratory (Ex: EMI, friction, fretting and other mechanical and hydraulic phenomena) based on the in-flight noise survey carried out by earlier investigators. From these experiments data was acquired and classified into their respective classes through MLP. Further, these noises were mixed together and clustered through KSOM and then classified into their respective clusters through MLP resulting in an accuracy of 95%- 100% Subsequently, to evaluate the utility of NAEDA for source classification and characterization, carbon fiber reinforced plastic (CFRP) specimens were subjected to spectrum loading simulating typical in-flight load and AE signals were acquired continuously up to a maximum of three designed lives and in some cases up to failure. Further, AE signals with similar characteristics were grouped into individual clusters through self-organizing map and labeled as belonging to appropriate failure modes, there by generating the class configuration. Then MLP was trained with this class information, which resulted in automatic identification and classification of failure modes with an accuracy of 95% - 100%. In addition, extraneous noise generated during the experiments was acquired and classified so as to evaluate the presence or absence of such data in the AE data acquired from the CFRP specimens. In the next stage, noise and signals were mixed together at random and were reclassified into their respective classes through supervised training of multi-layer back propagation NN. Initially only noise was discriminated from the AE signals from CFRP failure modes and subsequently both noise discrimination and failure mode identification and classification was carried out resulting in an accuracy of 95% - 100% in most of the cases. Further, extraneous signals mentioned above were classified which indicated the presence of such signals in the AE signals obtained from the CFRP specimen. Thus, having established the basis for noise identification and AE source classification and characterization, two specific examples were considered to evaluate the utility and efficiency of NAEDA. In the first, with the postulation that different basic failure modes in composites have unique AE signatures, the difference in damage generation and progression can be clearly characterized under different loading conditions. To examine this, static compression tests were conducted on a different set of CFRP specimens till failure with continuous AE monitoring and the resulting AE signals were classified through already trained NAEDA. The results obtained shows that the total number of signals obtained were very less when compared to fatigue tests and the specimens failed with hardly any damage growth. Further, NAEDA was able to discriminate the"noise and failure modes in CFRP specimen with the same degree of accuracy with which it has classified such signals obtained from fatigue tests. In the second example, with the same postulate of unique AE signatures for different failure modes, the differences in the complexion of the damage growth and progression should become clearly evident when one considers specimens with different lay up sequences. To examine this, the data was reclassified on the basis of differences in lay up sequences from specimens subjected to fatigue. The results obtained clearly confirmed the postulation. As can be seen from the summary of the work presented in the foregoing paragraphs, the investigations undertaken within the scope of this thesis involve elaborate experimentation, development of tools, acquisition of extensive data and analysis. Never the less, the results obtained were commensurate with the efforts and have been fruitful. Of the useful results that have been obtained, to state in specific, the first is, discrimination of simulated noise sources achieved with significant success but for some overlapping which is not of major concern as far as noises are concerned. Therefore they are grouped into required number of clusters so as to achieve better classification through supervised NN. This proved to be an innovative measure in supervised classification through back propagation NN. The second is the damage characterization in CFRP specimens, which involved imaginative data processing techniques that proved their worth in terms of optimization of various training parameters and resulted in accurate identification through clustering. Labeling of clusters is made possible by marking each signal starting from clustering to final classification through supervised neural network and is achieved through phenomenological correlation combined with ultrasonic imaging. Most rewarding of all is the identification of failure modes (AE signals) mixed in noise into their respective classes. This is a direct consequence of innovative data processing, multi-class clustering and flexibility of grouping various noise signals into suitable number of clusters. Thus, the results obtained and presented in this thesis on NN approach to AE signal analysis clearly establishes the fact that methods and procedures developed can automate detection and identification of failure modes in CFRP composites under hostile environment, which could lead to the development of an in-flight monitoring system.

In recent times, light gauge cold-formed steel sections have been used extensively in residential, industrial and commercial buildings as primary load bearing structural components. This is because cold-formed steel sections have a very high strength to weight ratio compared with thicker hot-rolled steel sections, and their manufacturing process is simple and cost-effective. However, these members are susceptible to various buckling modes including local and distortional buckling and their ultimate strength behaviour is governed by these buckling modes. Fire safety design of building structures has received greater attention in recent times due to continuing loss of properties and lives during fires. Hence, there is a need to fully evaluate the performance of light gauge cold-formed steel structures under fire conditions. Past fire research has focused heavily on heavier, hot-rolled steel members. The buckling behaviour of light gauge cold-formed steel members under fire conditions is not well understood. The buckling effects associated with thin steels are significant and have to be taken into account in fire safety design. Therefore, a research project based on extensive experimental and numerical studies was undertaken at the Queensland University of Technology to investigate the distortional buckling behaviour of light gauge cold-formed steel compression members under simulated fire conditions. As the first phase of this research program more than 115 tensile coupon tests of light gauge cold-formed steels including two steel grades and five thicknesses were conducted at elevated temperatures. Accurate mechanical properties including the yield strength, elasticity modulus and stress-strain curves were all determined at elevated temperatures since the deterioration of the mechanical properties is one of the major parameters in the structural design under fire conditions. An appropriate stress-strain model was also developed by considering the inelastic characteristics. The results obtained from the tensile coupon tests were then used to predict the ultimate strength of cold-formed steel compression members. In the second phase of this research more than 170 laboratory experiments were undertaken to investigate the distortional buckling behaviour of light gauge coldformed steel compression members at ambient and elevated temperatures. Two types of cross sections were selected with various thicknesses (nominal thicknesses are 0.6, 0.8, and 0.95 mm) and both low and high strength steels (G250 and G550 steels with minimum yield strengths of 250 and 550 MPa). The experiments were conducted at six different temperatures in the range of 20 to 800°C. A finite element model of the tested compression members was then developed and validated with the help of experimental results. The degradation of mechanical properties with increasing temperatures was included in finite element analyses. An extensive series of parametric analyses was undertaken using the validated finite element model to investigate the effect of all the influential parameters such as section geometry, steel thickness and grade, mechanical properties and temperature. The resulting large data base of ultimate loads of compression members subject to distortional buckling was then used to review the adequacy of the current design rules at ambient temperature. The current design rules were reasonably accurate in general, but in order to improve the accuracy further, this research has developed new design equations to determine the ultimate loads of compression members at ambient temperature. The developed equation was then simply modified by including the relevant mechanical properties at elevated temperatures. It was found that this simple modification based on reduced mechanical properties gave reasonable results, but not at higher temperatures. Therefore, they were further modified to obtain a more accurate design equation at elevated temperatures. The accuracy of new design rules was then verified by comparing their predictions with the results obtained from the parametric study. This thesis presents a description of the experimental and numerical studies undertaken in this research and the results including comparison with simply modified current design rules. It describes the laboratory experiments at ambient and elevated temperatures. It also describes the finite element models of cold-formed steel compression members developed in this research that included the appropriate mechanical properties, initial geometric imperfections and residual stresses. Finally, it presents the details of the new design equations proposed for the light gauge coldformed steel compression members subjected to distortional buckling effects at elevated temperatures.

Cold-formed steel members have been widely used in residential, industrial and commercial buildings as primary load bearing structural elements and non-load bearing structural elements (partitions) due to their advantages such as higher strength to weight ratio over the other structural materials such as hot-rolled steel, timber and concrete. Cold-formed steel members are often made from thin steel sheets and hence they are more susceptible to various buckling modes. Generally short columns are susceptible to local or distortional buckling while long columns to flexural or flexural-torsional buckling. Fire safety design of building structures is an essential requirement as fire events can cause loss of property and lives. Therefore it is essential to understand the fire performance of light gauge cold-formed steel structures under fire conditions. The buckling behaviour of cold-formed steel compression members under fire conditions is not well investigated yet and hence there is a lack of knowledge on the fire performance of cold-formed steel compression members. Current cold-formed steel design standards do not provide adequate design guidelines for the fire design of cold-formed steel compression members. Therefore a research project based on extensive experimental and numerical studies was undertaken at the Queensland University of Technology to investigate the buckling behaviour of light gauge cold-formed steel compression members under simulated fire conditions. As the first phase of this research, a detailed review was undertaken on the mechanical properties of light gauge cold-formed steels at elevated temperatures and the most reliable predictive models for mechanical properties and stress-strain models based on detailed experimental investigations were identified. Their accuracy was verified experimentally by carrying out a series of tensile coupon tests at ambient and elevated temperatures. As the second phase of this research, local buckling behaviour was investigated based on the experimental and numerical investigations at ambient and elevated temperatures. First a series of 91 local buckling tests was carried out at ambient and elevated temperatures on lipped and unlipped channels made of G250-0.95, G550-0.95, G250-1.95 and G450-1.90 cold-formed steels. Suitable finite element models were then developed to simulate the experimental conditions. These models were converted to ideal finite element models to undertake detailed parametric study. Finally all the ultimate load capacity results for local buckling were compared with the available design methods based on AS/NZS 4600, BS 5950 Part 5, Eurocode 3 Part 1.2 and the direct strength method (DSM), and suitable recommendations were made for the fire design of cold-formed steel compression members subject to local buckling. As the third phase of this research, flexural-torsional buckling behaviour was investigated experimentally and numerically. Two series of 39 flexural-torsional buckling tests were undertaken at ambient and elevated temperatures. The first series consisted 2800 mm long columns of G550-0.95, G250-1.95 and G450-1.90 cold-formed steel lipped channel columns while the second series contained 1800 mm long lipped channel columns of the same steel thickness and strength grades. All the experimental tests were simulated using a suitable finite element model, and the same model was used in a detailed parametric study following validation. Based on the comparison of results from the experimental and parametric studies with the available design methods, suitable design recommendations were made. This thesis presents a detailed description of the experimental and numerical studies undertaken on the mechanical properties and the local and flexural-torsional bucking behaviour of cold-formed steel compression member at ambient and elevated temperatures. It also describes the currently available ambient temperature design methods and their accuracy when used for fire design with appropriately reduced mechanical properties at elevated temperatures. Available fire design methods are also included and their accuracy in predicting the ultimate load capacity at elevated temperatures was investigated. This research has shown that the current ambient temperature design methods are capable of predicting the local and flexural-torsional buckling capacities of cold-formed steel compression members at elevated temperatures with the use of reduced mechanical properties. However, the elevated temperature design method in Eurocode 3 Part 1.2 is overly conservative and hence unsuitable, particularly in the case of flexural-torsional buckling at elevated temperatures.

We used RUSI (rehabilitative ultrasound imaging) for measurement of abdominal and back muscle in different loading modes. Methods: We used diagnostic ultrasonography imaging for taking linear measurement of trunk muscles. We measured anterioposterior (AP) dimensions of lateral abdominal wall muscles- m. OE, m.OI, m.TrA and cross-section area (CSA) of lumbar m. multifidus. We compared two groups of sports-floorball players and sportsman using climbing and hanging (climbers, aerialists). We measured positions with compressive force (kneeling on all four with lifted knees) and with traction load (hang with upper limbs with flexion of lower limbs-with leg support and without). Study is made on 50 volunteers. Results: The pattern of thickness of abdominal muscles is same in all positions in both groups. The lowest is always AP thickness of m. TrA, wider is m. OE and the widest always m. OI. The resting thickness are in both groups almost in all cases the lowest. AP thickness in m. TrA in floorball players is only exception, there is lowest in hang without legs support. In all other case sis resting position always lowest. For m. OE are results same for both climbers and floorball players-the lowest thickness is in hang with legs support (floorball players 0,84 cm, climbers 0,87), greater activationis...