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 …

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.

Network layer indications are not readily available upon a link change; therefore, general dependes on the network layer may introduce unnecessary delays due to network layer signaling for a simple link layer handover. If information could be gathered at link layer to determine the need for network layer signaling, then both the delay and signaling load could be really improved over the current standards of Mobile IP.

This paper presents a Cross-layer decision on two layer network and link layers to improve the performance of Enhanced Mobile IP (E-Mobile IP) handover in which reducing packet loss and latency during handover process.

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.

Cutting samples (n = 93) from the Sirte, Tagrifet, Rakb, Rachmat, Bahi Formations of Upper Cretaceous and Nubian Formation (Lower Cretaceous) derived from eleven wells (6C1-59, 6J1-59, 6R1-59, KK1-65, OO2-65, M1-51, KK1-65, B-96, B-95, B-99, E1-NC-59) locate in the Amal, Gialo, Nafoora, and Sarir Fields present in East Sirte Basin were analysed in the aim of their organic geochemical evaluation. A bulk geochemical parameters and evaluation of specific biomarkers by chromatography-mass spectrometry (GC-MS) implemented to find out a diversity of interbedded non-marine lithofacies including sandstones, siltstones, shales and conglomerates. Such rocks are good source and contain fair to good contented of organic matter passing in the course of very good, in which the excellent source rocks have organic carbon richness (TOC) reached to 5.16 wt%. The studied samples are ranged from gas to oil-prone organic matter (OM) of hydrogen index (HI) ranged between 115 - 702 mg HC/g TOC, related with gas prone (OM) of (HI) <150 and most beds contain oil-prone organic matter of (HI) > 300, associated with oxygen index (OI): 3 - 309 mg CO2/g TOC indicate that organic matter is dominated by Type II/III kerogen. The maturity of these source rocks is variations ranges from mature to post-mature-oil window in the Sirte and Rachmat Formations, as inferred from the production index (PI: 0.07 - 1.55) and Tmax and Ro% data (Tmax: 425 - 440/Ro%: 0.46 - 1.38) and early to mid-stage maturities for the other formations. Low PI in some samples seems to imply that the most of the hydrocarbons have expelled and migrated from the rocks. Biomarker ratios of individual hydrocarbons in rock extracts (n = 21), were also used in order to investigate the samples’ thermal maturity and palaeo depositional conditions. Pristine/Phytane ratios of 0.65 - 1.25 and dibenzothiophene to phenanthrene (DBT/P) ratios of 0.04 - 0.47 indicated Anoxic and suboxic conditions of depositional source rock. The origin of OM of the studied samples attributed to a marine algal source as indicated from the dominated by the C27 and co-dominant C28 homologues sterane in molecular composition distributions. The marine shale and carbonate lithofacies of rock samples were also indicated by high C19TT/C23TT ratio and low relative abundance of C24TeT/C23TT, consistent with their interpreted marine affinity. An organic geochemical evaluation pointed out that the Sirte Shale formation (Campanian/Turonian) is the main source rock in this petroleum area.