The three-parameter lognormal distribution is widely used in many areas of science. Some modifications have been proposed to improve the maximum likelihood estimator. In some cases, however, the modified maximum likelihood estimates do not exist or the procedure encounters multiple estimates. The purpose of this research is focused on estimating the threshold or location parameter , because when is known, then the other two estimated parameters are obtained from the first two MLE equations. In this research, a method for constructing confidence intervals, confidence limits, and point estimator for the threshold parameter is proposed. Monte-Carlo simulation, bisection method, and SAS/IML were used to accomplish this objective. The bias of the point estimator and mean square error (MSE) criteria were used throughout extensive simulation to evaluate the performance of the proposed method. The result shows that the proposed method can provide quite accurate estimates.

Bridge Information Modeling (BrIM), as a concept, has been introduced to enhance the procedures of the whole phases of a bridge life-cycle starting with concept and design, through construction and operation, and ending with maintenance and rehabilitation. Integrating BrIM and design tools will help improve the methods used in designing and constructing bridge projects at early stages taking into considerations their cost and time constraints. The main objective of this research is to develop an integrated model that helps owners, designers and construction managers visualize bridge projects in a 3D mode and accordingly automate the process of generating preliminary cost estimates during the conceptual design stage. To achieve the above mentioned objective, a proposed methodology will be applied where all the aspects needed to develop an efficiently integrated model are realized. Thus, this research describes the proposed methodology that incorporates three modules, which are the core of the integrated model. These modules are: a knowledge based system module, which is used to generate conceptual dimensions and parameters depending on algorithmic and heuristic knowledge gathered from codes, guidelines and design experts; a conceptual cost estimation module, which is used to generate conceptual cost estimate based on the results of the first module; and a 3DCAD module, which is integrated with the previous modules where users will visualize the proposed bridge in 3D based on the results of the knowledge based module. The successful development of the model would help owners and investors identify the cost and visualize the proposed projects at the early stages of the project life, so they have an idea of the budget required and the aesthetics of the proposed projects. The integrated model is validated through an actual case project to test its workability and output.

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Preconstruction services play a vital role in ensuring timely approval of infrastructure funds and successful execution of construction projects. Most state DOTs use simple methods such as a percentage of estimated construction costs that has proven to be unreliable. Several studies have developed statistical models using historical data to improve current practices. However, such models have performed poorly, and practitioners have not utilized these models. This study develops and evaluates data mining models such as multiple regression and artificial neural networks and concludes that such models do not provide sufficiently accurate estimates of preconstruction service fees and hours. Subsequently, it proposes an alternative approach using a case-based reasoning (CBR) technique that uses similarity scoring to retrieve the most similar projects. The historical preconstruction service fees and hours of similar projects can be used to estimate preconstruction service fees and hours for a new project and make any adjustment necessary. A spreadsheet tool is developed to implement this CBR technique. The tool provides a simple and flexible platform that enables engineers to extract necessary data and help them in making data-driven estimates. Thus, the tool is expected to aid state DOT engineers in negotiating with consultants with higher confidence.

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This thesis is the result of many years of research in the field of manufacturing cost modelling. It particularly focuses on the Data Collection Process for the development of manufacturing cost models in the UK Aerospace Industry with no less important contributions from other areas such as construction, process and software development. The importance of adopting an effective model development process is discussed and a new CMD Methodology is proposed. In this respect, little research has considered the development of the cost model from the point of view of a standard and systematic Methodology, which is essential if an optimum process is to be achieved. A Model Scoping 3 Framework, a functional Data Source and Data Collection Library and a referential Data Type Library are the core elements of the proposed Cost Model Development Methodology. The research identified a number of individual data collection methods, along with a comprehensive list of data sources and data types, from which essential data for developing cost models could be collected. A Taxonomy based upon sets of generic characteristics for describing the individual data collection, data sources and data types was developed. The methods, tools and techniques were identified and categorised according to these generic characteristics. This provides information for selecting between alternative methods, tools and techniques. The need to perform frequent iterations of data collection, data identification, data analysis and decision making tasks until an acceptable cost model has been developed has become an inherent feature of the CMDP. It is expected that the proposed model scoping framework will assist cost engineering and estimating practitioners in: defining the features, activities of the process and the attributes of the product for which a cost model is required, and also in identifying the cost model characteristics before the tasks of data identification and collection start. It offers a structured way of looking at the relationship between data sources, cost model characteristics and data collection tools and procedures. The aim was to make the planning process for developing cost models more effective and efficient and consequently reduce the time to generate cost models.

Annual average daily traffic (AADT) is used to estimate intersection performance across Kentucky. The Kentucky Transportation Cabinet (KYTC) currently collects AADTs for state maintained roads, but lacks this information on local roads. A method is needed to estimate local road AADTs in a cost-effective and reasonable manner. A literature review was conducted on AADT models and found no models suitable to Kentucky. Therefore an AADT model using non-linear regression was developed for local roads in Kentucky This model divided the state into three regions utilizing Kentucky’s highway districts. This partitioning accounted for geographic and socioeconomic variability across the state. Each regional model relied upon three independent variables: probe count, residential vehicle registration, and curve rating. HERE proprietary probe counts provide tracking visibility on a select portion of vehicles moving across Kentucky highways. Residential vehicle registrations were used to estimate trip generation information. Finally, the curve rating partially indicates accessibility. The models were adjusted to KYTC daily vehicle miles traveled (DVMT) county control totals for local roads. Sensitivity analysis was conducted to examine the impact of model errors for use in intersection safety analysis. Results indicate that the estimates generated can be effectively used for safety assessment and countermeasure prioritization.

Developing accurate construction estimates is critical for electric utilities to make reliable financial plans for their future. Parametric estimating is just one of several techniques available to help estimate the cost of a construction project. Other estimating methods may have some advantages over parametric estimating in the latter stages of a project but parametric estimating is possibly the most accurate method in the very early stages of a project. This report delves into the analysis and development of a parametric equation for use primarily in the very early stages of a construction project. The result of this research is a functional equation that can be used for estimating future electric substation construction cost with a fair level of confidence. / text

Considerable amounts of event history data are collected through longitudinal surveys. These surveys have many particularities or features that are the results of the dynamic nature of the population under study and of the fact that data collected through longitudinal surveys involve the use of complex survey designs, with clustering and stratification. These particularities include: attrition, seam-effect, censoring, left-truncation and complications in the variance estimation due to the use of complex survey designs. This thesis focuses on the last two points. Statistical methods based on the stratified Cox proportional hazards model that account for intra-cluster dependence, when the sampling design is uninformative, are proposed. This is achieved using the theory of estimating equations in conjunction with empirical process theory. Issues concerning analytic inference from survey data and the use of weighted versus unweighted procedures are also discussed. The proposed methodology is applied to data from the U. S. Survey of Income and Program Participation (SIPP) and data from the Canadian Survey of Labour and Income Dynamics (SLID). Finally, different statistical methods for handling left-truncated sojourns are explored and compared. These include the conditional partial likelihood and other methods, based on the Exponential or the Weibull distributions.