Progressive building collapse occurs when failure of a structural component leads to the failure and collapse of surrounding members, possibly promoting additional collapse. Global system collapse will occur if the damaged system is unable to reach a new static equilibrium configuration. The objective of this research is to identify and investigate the importance of issues related to the unexpected collapse resulting from the loss of key elements of the structure (a column or columns, for example). In this research study there are examined on the entire frame including details of the column loss scenario and also the influence of the ductility and strength of beam-to-column joints adjacent to the location of such as incident. The study also includes catenary action in beams resulting from collapse and its impact on the adjacent joints and reports on the comparison of negative and positive moments in joints. In addition, it records the important notes associated with the state of collapse and parts of this research include the conformity of numerical and analytical results, the ratio of credibility and conformity of results and the test. This thesis reports in detail on the experimental and numerical investigations of the behaviour of steel and composite joints, which aim to develop common solutions with regard to the joint semi-rigid and partial strength properties that might allow for significant rotations and the redistribution of internal forces in structural system when there was a damage to its key element such as a column. Investigations of steel and composite joints with regard to their ductile behaviour are presented. Thereby special attention is paid to shaping of …

Genetic algorithms (GAs) are based on techniques inspired by some aspects of natural science such as inheritance, reproduction and mutation, and they are used as optimization technique for searching large solution spaces. In computer science, for example, they could be used in data sorting and searching, circuit design and to improve application performance the quality of designed tools such as code generation. This paper looks at the possibility of using genetic algorithms to ameliorate the automatic construction of code generators. Experimental evidence is provided that the use of such algorithms can improve the quality of automatically constructed code generators.

The development and implementation of condition monitoring system become very important for wind industry with the increasing number of failures in wind turbine generators due to over temperature especially in offshore wind turbines where higher maintenance costs than onshore wind farms have to be paid due to their farthest locations. Monitoring the wind generators temperatures is significant and plays a remarkable role in an effective condition monitoring system. Moreover, they can be easily measured and recorded automatically by the Supervisory Control and Data Acquisition (SCADA) which gives more clarification about their behavior trend. An unexpected increase in component temperature may indicate overload, poor lubrication, or possibly ineffective passive or active cooling. Many techniques are used to reliably predict generator's temperatures to avoid occurrence of failures in wind turbine generators. Multiple Linear Regression Model (MLRM) is a model that can be used to construct the normal operating model for the wind turbine generator temperature and then at each time step the model is used to predict the generator temperature by measuring the correlation between the observed values and the predicted values of criterion variables. Then standard errors of the estimate can be found. The standard error of the estimate indicates how close the actual observations fall to the predicted values on the regression line. In this paper, a new condition-monitoring method based on applying Multiple Linear Regression Model for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of an electrical generator temperatures based on the historical generator temperatures data. Case study built on a data collected from actual measurements demonstrates the adequacy of the proposed model.

Current and future applications of small gas turbine engines annular type combustors have requirements presenting difficult disputes to the combustor designer. Reduced cost and fuel consumption and improved durability and reliability as well as higher temperatures and pressures for such application are forecast. Coupled with these performance requirements; irrespective of the engine size; is the demand to control the pollutant emissions, namely the oxides of nitrogen, carbon monoxide, smoke and unburned hydrocarbons. These technical and environmental challenges have made the design of small size combustion system a very hard task. Thus, the main target of this work is to generalize a calculation method of annular type combustors for small gas turbine engines that enables to understand the fundamental concepts of the coupled processes and to identify the proper procedure that formulate and solve the problems in combustion fields in as much simplified and accurate manner as possible. The combustion chamber in task is designed with central vaporizing unit and to deliver 516.3 KW of power. The geometrical constraints are 142 mm & 140 mm overall length and casing diameter, respectively, while the airflow rate is 0.8 kg/sec and the fuel flow rate is 0.012 kg/sec. The relevant design equations are programmed by using MathCAD language for ease and speed up of the calculation process.

In this work, in addition to the 16-corrugation that have been tested, three more models with same dimensions and different corrugation density have been tested too. 18-corrugation, 20-corrugation, and 22-corrugation (RCCT-18, RCCT-20, and RCCT-22) have been investigated. Here, it is wise to mention that all corrugations have the same shape and dimensions. Moreover, 22 corrugations were found the maximum number of corrugations that can be fabricated in the tube circumference. In other words it was impossible to fabricate a tube with more than 22-corrugations at that certain diameter, since all tested composite tubes have the same length and diameter at all testing phases. Results show that corrugation density has an influence on the performance of composite shells as an energy absorber. It has been found that as corrugation density increases, total energy absorption increases.

Today the use of composite materials in different kinds of applications is accelerating rapidly. Composite materials have become common engineering materials and are designed and manufactured for various application. This paper involves an experimental program including testing the capabilities of hybrid material s (composite and metallic materials) as an energy absorber. The method of approach has been to fabricate and test a series of specimens. The specimens have cylindrical shape; they are fabricated using hand lay-up technique. Hybrid specimens involve two subtypes: metallic cylinder surrounded by three layers of composite cylinder referred as M/C, and three layers of composite cylinder form the internal part surrounded by metallic cylinder referred as C/M (Fig.1). Hybrid model are subjected to lateral quasi static compression load.

Pavement distress is an indication of pavement layer deterioration. There are many types of

deteriorations; however, rutting, defined as the permanent deformation forming longitudinal

surface depressions in the wheel paths, is one of the most important kind of distress that affect

the safety and the ride quality of flexible pavement. The main objective of this study was to

develop an empirical pavement rutting model for the wet freeze zone, which is one of the four

long term pavement performance (LTPP) climate zones, to predict the depth of pavement rutting

on granular bases. Using the LTPP database, the study aimed at a better understanding of the

pavement rutting phenomena and the factors that may affect pavement rutting. Multiple

regression analysis was performed to develop a flexible pavement rutting model. The proposed

model was developed based on the relationship between the response variable rut depth, and

predictor variables of traffic loads, structural number, Marshall stiffness, air voids in the total

mix, and voids in the mineral aggregate. It was found that traffic loads was the predominant

factor that have a significant effect on pavement rutting, which agrees with the existing

literature, as well as engineering knowledge and practice. Following the traffic loads, structural

number was the most significant secondary factor, followed by percent of voids in the total mix,

• voids in the mineral aggregate, and Marshall stiffness.

This research presents the effect of corrugation geometry on the crushing behavior, energy absorption, of woven roving glass fiber/epoxy laminated composite shells. Experimental investigations were carried out on three geometrical different types of composite shells subjected to compressive loadings. The results showed that the loading carrying capability is significantly influenced by corrugation geometry in axial crushing. Load–displacement curve was plotted for all conducted tests

neural network is considered as a nonlinear dynamic system consisting of a large number of simple processing elements interconnected in some manner with adjustable weighted strength. Neural networks provide qualitative and quantitative (analog, digital or logical) knowledge through information coding and decoding, and have powerful functions in learning and selforganization. These properties make neural networks considered to be more powerful in dealing with numerical data than other artificial intelligent systems like expert systems. However, the performance of neural networks depends deeply in number of factors including transfer function, number of hidden layers, number of nodes in hidden layers, input function, and weight function. In this article we present a comparative study of these factors and how they influence the performance of a system.