Rethinking construction cost overruns: an artificial neural network approach to construction cost estimation

The main concern of a construction client is to procure a facility that is able to meet its functional requirements, of the required quality, and delivered within an acceptable budget and timeframe. The cost aspect of these key performance indicators usually ranks highest. In spite of the importance of cost estimation, it is undeniably neither simple nor straightforward because of the lack of information in the early stages of the project. Construction projects therefore have routinely overrun their estimates. Cost overrun has been attributed to a number of sources including technical error in design, managerial incompetence, risk and uncertainty, suspicions of foul play and even corruption. Furthermore, even though it is accepted that factors such as tendering method, location of project, procurement method or size of project have an effect on likely final cost of a project, it is difficult to establish their measured financial impact. Estimators thus have to rely largely on experience and intuition when preparing initial estimates, often neglecting most of these factors in the final cost build-up. The decision-to-build for most projects is therefore largely based on unrealistic estimates that would inevitably be exceeded. The main aim of this research is to re-examine the sources of cost overrun on construction projects and to develop final cost estimation models that could help in reaching more reliable final cost estimates at the tendering stage of the project. The research identified two predominant schools of thought on the sources of overruns – referred to here as the PsychoStrategists and Evolution Theorists. Another finding was that there is no unanimity on the reference point from which cost performance could be assessed, leading to a large disparity in the size of overruns reported. Another misunderstanding relates to the term “cost overrun” itself. The experimental part of the research, conducted in collaboration with two industry partners, used a combination of non-parametric bootstrapping and ensemble modelling with artificial neural networks to develop final project cost models based on about 1,600 water infrastructure projects. 92% of the validation predictions were within ±10% of the actual final cost of the project. The models will be particularly useful at the pre-contract stage as they will provide a benchmark for evaluating submitted tenders and also allow the quick generation of various alternative solutions for a construction project using what-if scenarios. The original contribution of the study is a fresh thinking of construction “cost overruns”, now proposed to be more appropriately known as “cost growth” based on a synthesises of the two schools of thought into a conceptual model. The second contribution is the development of novel models of construction cost estimation utilising artificial neural networks coupled with bootstrapping and ensemble modelling

Artificial intelligence applications in waste water monitoring for industrial purposes

This thesis reports on work carried out in the development of software for artificial intelligence sensing systems based on UV-Vis spectroscopy, designed for remote on-line and real-time analysis for monitoring of industrial effluent. A feasibility study on artificial intelligence methods and the design of an intelligent monitoring system has been researched. This system is capable of detecting the occurrences of chemical pollutants and the concentration of species involved. The controlling software was developed in this work for the remote modem control of a computer controlled UV-Vis spectrometer system. This provides facilities for signal processing, data storage, and transfer of data to a host machine for real-time analysis. This gives significant advantages in term of automatically and instantly reporting of a pollution incident. This front end sensing system has been installed at industrial sites in order to demonstrate the apparatus in the real situations and to obtain data for qualitative analysis. Difficulties in working with the above data pointed to the need for a laboratory-based evaluation and modelling of analysis methods. This evaluation and development of suitable methods forms a major part of the work. The samples prepared for a set of data were mixtures of nitrate, hypochlorite and ammonia in various concentrations representatives of that expected in real outflows. This data set presented several significant problems in data analysis, including an overlap of UV absorption bands and the interaction between ammonia and hypochlorite to form monochloramine which has its own specific spectral features. In the evaluation, the spectral data obtained were analysed by two different methods. The first was Principle Component Analysis (PCA) which is based on linear multivariate analysis, and samples were investigated to compare the effects of interactions between components. The second method was Neural Network analysis, which is a non-linear analysis technique. After considerable effort, this approach resulted in a data analysis scheme where the Back-Propagation algorithm was used as a two-step process. In the first-step, the network inputs were derived by binary encoding segments of the second derivative of the absorption spectra according to their shape and the network outputs specified according to which species were likely to occur. As a result, the second-step network could then focus on a few inputs that strongly correlate with the presence of the expected species. Also the second-step provided a filter that compensated for false classification of species, at low concentration levels. The resulting data analysis scheme depends on a knowledge of the expected chemistry for implementation: however it gives a much better performance than PCA in this particular case. The complete monitoring system has been integrated with a Graphical User Interface software to perform real time analysis at a host machine. A multi-task system for on-line monitoring of data transmitted from a remote site, has been developed, based on the neural network approach. Finally, the intelligent monitoring system is demonstrated and evaluated

Simple low cost causal discovery using mutual information and domain knowledge

PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the insights they can provide regarding the situation under study. Their use of prior knowledge and the causal knowledge produced by the learners are examined. Current causal learning algorithms are discussed in terms of their strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN that operates with a polynomial time complexity in both the number of variables and records examined. It makes no prior assumptions about the form of the relationships and is capable of making extensive use of available domain information. This learner is compared to a number of current learning algorithms and it is shown to be competitive with them

Fishing as a way of life: a cultural geography of fishing communities in Castletownbere (Ireland) and Le Guilvinec (Brittany)

Using the lens of contemporary cultural geography, this research develops an understanding of fishing as a way of life in Castletownbere (County Cork) and Le Guilvinec (Brittany) through relational and reciprocal processes. Drawing on the hermeneutics of both Gadamer and Ricoeur, I argue that pre-understandings are essential to the awareness of ‘Self’ and ‘Other’. This approach fuses different strands of cultural geography, including a focus on experiential enquiry, mobilities and motion arenas, religion and rituals, emotional and affective geographies. I explore how fishing families and their communities experience and give meaning to their being-in-their-world. Enhanced interpretations of the meanings that participants give to their maritime environment emerge through my encounters with three fishing realms − the home, the boat, and the pier. The immersive character of ethnographic methodology allows for a meaningful engagement with participants and their maritime environment. The insights generated from these encounters provide new and emerging narratives of the lifeworlds of Castletownbere and Le Guilvinec. By focusing my study on the experiences and practices of the different performances and activities of two fishing communities in Ireland and Brittany, this research produces rich and novel understandings of fishing as a way of life and contributes to the debates concerned with people-place relationships and how these people construct and maintain senses of identity and place

River and coast: regionality in North Kimberley rock art

The aim of this thesis is to examine regionality in the rock art of the north Kimberley, Western Australia. The region is renowned for its art of polychrome Wandjina figures, totemic ancestors and creators of the land for modern West Kimberley people. Underlying them are smaller, elegantly painted human figures. These are Bradshaw Figures or the Gwion Gwion as they are increasingly being called. The figures are decorated as if for dancing with waist mounted tassels, sashes and elaborately decorated headdresses, and an elaborate stylistic chronology has been prepared for the Kimberley art sequence. What is missing from the literature and what this thesis aims to fulfil, is knowledge of regionality and changes in the distribution of the body of art. Some the earliest art is from what I term the Early Phase and is thought to date to a time of aridity near the height of the ice age in Australia. Successive art periods may have occurred at times of changing climate as sea levels rose at the end of the ice age and the ensuing flooding of the exposed coastal plain. The sea level and the shoreline only stabilised in its present day position, and the present climate and environment settled to its current conditions, around 6500 years ago. I argue that the different styles of art and different locations selected in which to paint are related to the situation in the period of flux, when the inhabitants of the Kimberley were affected by changes, including the changes in their territory due to rising sea levels. Two geographically distinct areas were selected which would have been different at the time of painting of the earlier art, one being a river and the other, the coast, as at the time of painting the elegant figures, with retreating shorelines, it would have been inland. My research shows that the painters of Middle Phase art oscillated between permanent water and more transient sources, an effect influenced by their experience of ancient changes in climate

Study of the Soil Water Movement in Irrigated Agriculture

In irrigated agriculture, the study of the various ways water infiltrates into the soils is necessary. In this respect, soil hydraulic properties, such as soil moisture retention curve, diffusivity, and hydraulic conductivity functions, play a crucial role, as they control the infiltration process and the soil water and solute movement. This Special Issue presents the recent developments in the various aspects of soil water movement in irrigated agriculture through a number of research topics that tackle one or more of the following challenges: irrigation systems and one-, two-, and three-dimensional soil water movement; one-, two-, and three-dimensional infiltration analysis from a disc infiltrometer; dielectric devices for monitoring soil water content and methods for assessment of soil water pressure head; soil hydraulic properties and their temporal and spatial variability under the irrigation situations; saturated–unsaturated flow model in irrigated soils; soil water redistribution and the role of hysteresis; soil water movement and drainage in irrigated agriculture; salt accumulation, soil salinization, and soil salinity assessment; effect of salts on hydraulic conductivity; and soil conditioners and mulches that change the upper soil hydraulic properties and their effect on soil water movement

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered

Utah Heavy Oil Program

The Utah Heavy Oil Program (UHOP) was established in June 2006 to provide multidisciplinary research support to federal and state constituents for addressing the wide-ranging issues surrounding the creation of an industry for unconventional oil production in the United States. Additionally, UHOP was to serve as an on-going source of unbiased information to the nation surrounding technical, economic, legal and environmental aspects of developing heavy oil, oil sands, and oil shale resources. UHOP fulGilled its role by completing three tasks. First, in response to the Energy Policy Act of 2005 Section 369(p), UHOP published an update report to the 1987 technical and economic assessment of domestic heavy oil resources that was prepared by the Interstate Oil and Gas Compact Commission. The UHOP report, entitled 'A Technical, Economic, and Legal Assessment of North American Heavy Oil, Oil Sands, and Oil Shale Resources' was published in electronic and hard copy form in October 2007. Second, UHOP developed of a comprehensive, publicly accessible online repository of unconventional oil resources in North America based on the DSpace software platform. An interactive map was also developed as a source of geospatial information and as a means to interact with the repository from a geospatial setting. All documents uploaded to the repository are fully searchable by author, title, and keywords. Third, UHOP sponsored Give research projects related to unconventional fuels development. Two projects looked at issues associated with oil shale production, including oil shale pyrolysis kinetics, resource heterogeneity, and reservoir simulation. One project evaluated in situ production from Utah oil sands. Another project focused on water availability and produced water treatments. The last project considered commercial oil shale leasing from a policy, environmental, and economic perspective