To create hydrologically sustainable wetlands, knowledge of the water use requirements of target habitats must be known. Extensive literature reviews highlighted a dearth of water-use data associated with large reedbeds and wet woodland habitats and in response to this field experiments were established. Field experiments to measure the water use rates of large reedbeds [ET(Reed)] were completed at three sites within the UK. Reference Crop Evapotranspiration [ETo] was calculated and mean monthly crop coefficients [Kc(Reed)] were developed. Kc(Reed) was less than 1 during the growing season (March to September), ranging between 0.22 in March and reaching a peak of 0.98 in June. The developed coefficients compare favourably with published data from other large reedbed systems and support the premise that the water use of large reedbeds is lower than that from small/fringe reedbeds. A methodology for determining water use rates from wet woodland habitats (UK NVC Code: W6) is presented, in addition to provisional ET(W6) rates for two sites in the UK. Reference Crop Evapotranspiration [ETo] data was used to develop Kc(W6) values which ranged between 0.89 (LV Lysimeter 1) and 1.64 (CH Lysimeter 2) for the period March to September. The data are comparable with relevant published data and show that the water use rates of wet woodland are higher than most other wetland habitats. Initial observations suggest that water use is related to the habitat’s establishment phase and the age and size of the canopy tree species. A theoretical case study presents crop coefficients associated with wetland habitats and provides an example water budget for the creation of a wetland comprising a mosaic of wetland habitats. The case study shows the critical role that the water use of wetland habitats plays within a water budget.

This thesis focuses on the investigation of the abrasion resistance of fibre reinforced concrete floors at both the macro and micro levels. A literature review of the available literature concerning subjects allied to the current project is included. This highlights themes relevant to wear mechanisms and the factors influencing it: factors that affect the abrasion resistance of concrete and several test methods for assessing it; and the historical development of fibres and the properties of different fibre types and their influence on concrete. Three accelerated abrasion testers were compared and critically discussed for their suitability for assessing the abrasion resistance of concrete floors. Based on the experimental findings one accelerated abrasion apparatus was selected as more appropriate to be used for carrying out the main investigations. The laboratory programme that followed was undertaken to investigate the influence of various material and construction factors on abrasion resistance. These included mix variations (w/c ratio), fibre reinforcement, geometry, type and volume, curing method and superplasticizing agents. The results clearly show that these factors significantly affected abrasion resistance and several mechanisms were presumed to explain and better understand these observations. To verify and understand these mechanisms that are accountable for the breakdown of concrete slabs, the same concrete specimens that were used for the macro-study, were also subjected to microstructutural investigations using techniques such as Microhardness examination, Mercury intrusion porosimetry and Petrographic examination. It has been found that the abrasion resistance of concrete is primarily dependent on the microstructure and porosity of the concrete nearest to the surface. The feasibility of predicting the abrasion resistance of fibre reinforced concrete floors by indirect and non-destructive methods was investigated using five methods that have frequently been used for assessing the quality of concrete. They included the initial surface absorption test, the impact test, ball cratering, the scratch test and the base hardness test. The impact resistance (BRE screed tester) and scratch resistance (Base hardness tester) were found to be the most sensitive to factors affecting abrasion resistance and hence are considered to be the most appropriate testing techniques.

Urban regions present some of the most challenging areas for the remote sensing community. Many different types of land cover have similar spectral responses, making them difficult to distinguish from one another. Traditional per-pixel classification techniques suffer particularly badly because they only use these spectral properties to determine a class, and no other properties of the image, such as context. This project presents the results of the classification of a deeply urban area of Dudley, West Midlands, using 4 methods: Supervised Maximum Likelihood, SMAP, ECHO and Unsupervised Maximum Likelihood. An accuracy assessment method is then developed to allow a fair representation of each procedure and a direct comparison between them. Subsequently, a classification procedure is developed that makes use of the context in the image, though a per-polygon classification. The imagery is broken up into a series of polygons extracted from the Marr-Hildreth zero-crossing edge detector. These polygons are then refined using a region-growing algorithm, and then classified according to the mean class of the fine polygons. The imagery produced by this technique is shown to be of better quality and of a higher accuracy than that of other conventional methods. Further refinements are suggested and examined to improve the aesthetic appearance of the imagery. Finally a comparison with the results produced from a previous study of the James Bridge catchment, in Darleston, West Midlands, is made, showing that the Polygon classified ATM imagery performs significantly better than the Maximum Likelihood classified videography used in the initial study, despite the presence of geometric correction errors.

Particle impacts are of fundamental importance in many areas and there has been a renewed interest in research on particle impact problems. A comprehensive investigation of the particle impact problems, using finite element (FE) methods, is presented in this thesis. The capability of FE procedures for modelling particle impacts is demonstrated by excellent agreements between FE analysis results and previous theoretical, experimental and numerical results. For normal impacts of elastic particles, it is found that the energy loss due to stress wave propagation is negligible if it can reflect more than three times during the impact, for which Hertz theory provides a good prediction of impact behaviour provided that the contact deformation is sufficiently small. For normal impact of plastic particles, the energy loss due to stress wave propagation is also generally negligible so that the energy loss is mainly due to plastic deformation. Finite-deformation plastic impact is addressed in this thesis so that plastic impacts can be categorised into elastic-plastic impact and finite-deformation plastic impact. Criteria for the onset of finite-deformation plastic impacts are proposed in terms of impact velocity and material properties. It is found that the coefficient of restitution depends mainly upon the ratio of impact velocity to yield Vni/Vy0 for elastic-plastic impacts, but it is proportional to [(Vni/Vy0)*(Y/E*)]-1/2, where Y /E* is the representative yield strain for finite-deformation plastic impacts. A theoretical model for elastic-plastic impacts is also developed and compares favourably with FEA and previous experimental results. The effect of work hardening is also investigated.

A procedure has been developed which measures the settling velocity distribution of particles within a complete sewage sample. The development of the test method included observations of particle and liquid interaction using both synthetic media and sewage. Comparison studies with two other currently used settling velocity test procedures was undertaken. The method is suitable for use with either DWF or storm sewage. Information relating to the catchment characteristics of 35 No. wastewater treatment works was collected from the privatised water companies in England and Wales. 29 No. of these sites were used in an experimental programme to determine the settling velocity grading of 33 No. sewage samples. The collected data were analysed in an attempt to relate the settling velocity distribution to the characteristics of the contributing catchment. Statistical analysis of the catchment data and the measured settling velocity distributions was undertaken. A curve fitting exercise using an S-shaped curve which had the same physical characteristics as the settling velocity distributions was performed. None of these analyses found evidence that the settling velocity distribution of sewage had a significant relationship with the chosen catchment characteristics. The regression equations produced from the statistical analysis cannot be used to assist in the design of separation devices. However, a grading curve envelope was produced, the limits of which were clearly defined for the measured data set. There was no evidence of a relationship between settling velocity grading and the characteristics of the contributing catchment, particularly the catchment area. The present empirical approach to settling tank design cannot be improved upon at present by considering the variation in catchment parameters. This study has provided a basis for future research into the settling velocity measurement and should be of benefit to future workers within this field.

D.Phil. / This is a workbook to record the processes, responsible departments; current practices; business rules; inputs and outputs; and disconnects with regards to a “staple yourself to an order” exercise. This is done to establish problem areas within the supply chain and to provide guidance on improving the supply chain. This workbook should be used in conjunction with GISDataSCOR v1.0. and the results of the disconnect analysis. The “staple yourself to an order” exercise should start with SOURCE, MAKE, DELIVER, then followed by PLAN and RETURN.

The topic of my work is about how the product is getting from designers to final customer. As logistic is a collection of many different activities we talk about production, warehousing, transport and final presentation to the customer. But nowadys logistics is not only about material flow but about the information flow as well. That is why we cannot forget to mantion the communication with the client and getting his response.

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The aftermarket holds a vital role in the Volvo Group value offer. Producing profitability by satisfying the customers needs for important spare parts, ensuring maximum uptime for the entire range of vehicles produced and sold. As the cost for keeping stock exponentially increases with a higher availability, the availability can never be 100%. This in effect means that there will be occasions where an order is placed on a part that is currently not in stock, creating a back order. And while not all of these back orders can be avoided completely, predicting them before they occur will allow for preemptive measures to be taken, potentially reducing lead times and costs. Deep learning is a sub-section of machine learning, the study of methods to make computers find complex patterns in data. Deep learning has had an increase in popularity as the computational power and available data has greatly increased in recent years and is something that Volvo sees potential in. This creates the aim of this study which is to develop a deep learning model to predict the occurrence of back orders. In order to fulfill this aim, two main research questions were formed. The first research question intends to find underlying causes and factors that can explain the occurrence of back orders, in order to create the input features that the model can be trained on. This was initiated with a basis in literature, where a theoretical framework was created from different areas in the field of logistics as well as previous studies that combine logistics and machine learning. After this an empirical study was conducted where four previous initiatives from Volvo were found, that aim to explain the occurrence of back orders. As this was concluded, the findings were combined and synthesized into a list of factors that explain the underlying causes of back orders. In the second research question the factors listed were translated into input features of the model, where all quantifiable factors that could be and located in the Volvo database were included. This created the data set used to train the deep learning model to predict back orders. After the feature creation was completed, the actual design and development of the model could commence. Based on literature concerning deep learning along with directives from Volvo, a deep recurrent neural network was developed. The exact size and shape of the model was varied and evaluated to find the best performance. Evaluating the results showed several interesting findings. After training the model on one year of weekly data for 20 000 part numbers, the model proved to be skillful in predicting the occurrence of back orders. The model was able to predict 73% of back orders one week before they occurred (recall), and 72% of what the model deemed to be back orders were actual back orders (precision). The main challenges with predicting back orders were the imbalance between back order and a non-back order and the limit of one year of data. As the nature of back orders is that on average, only a few weeks per year will there be a back order on a given part, the training of the model becomes difficult. The difficulty with this imbalance is that the model is always less likely to predict a back order if the occurrence of back order itself is rare. The advantage of deep learning can be found with a large amount of data, and not being limited to one year of data is likely to produce better results. Despite these difficulties the model was highly successful in predicting the occurrence of back orders.