The level of achieved accuracy in design cost estimating is generally accepted by researchers as being less than desirable. Low accuracy has been attributed to the nature of historical cost data, estimating method and the expertise of the estimator. Previous researchers have suggested that the adoption of resource based estimating by designers could eliminate data and method-related problems. The work in this thesis has shown that this will not solve the problem of inaccuracy in estimating. A major problem in assessing accuracy in design cost estimating has been the absence of a generally agreed definition of the'true cost' of a construction project. Hitherto, studies of accuracy in design cost estimating have relied solely on the assessment of errors using the low bid as a datum. Design cost estimators do not always focus on predicting the low bid. Rather, they may focus on the lowest, second lowest, third lowest or any other bid, mean/median of bids, or sometimes, on just being'within the collection'. This has resulted in designers and researchers having different views on the level of achieved accuracy in estimating. To resolve this problem, an analysis package, ACCEST (ACCuracy in ESTimating), was developed to facilitate 'fair' assessment of accuracy in design cost estimates. Tests - using cost data from 7 offices, the ACCEST package and the OPEN ACCESS II package on an IBM PS/2 - have shown that error in design cost estimating (averaging 3.6% higher than the predicted parameter) is much lower than portrayed in construction literature (averagel3% higher than the low bid). Also, false associations between project environment factors (such as geographical location, market conditions, number of bidders, etc.) and the level of achieved accuracy has been developed by researchers through using the low bid as a datum. Previous researches have also demonstrated that design estimators do not learn sufficiently from experience on past projects. A controlled experiment on design cost estimating information selection was designed to explain this occurrence. Failure to learn, and the persistent use of information on one project for estimating, has been shown to result from the method of information storage in design offices, the illusion of validity of inaccurate rules and over-confidence resulting from inaccurate assessment of individual expertise. A procedure for aiding learning from experience in design cost estimating has been suggested. Finally, the work has shown that by distinguishing between different trades, and selectively applying different estimating strategies, based on the objective evaluation of the uncertainty associated with cost prediction for ear h trade, error in design cost estimating could be further reduced. Two formulae for predicting tender prices using data generated from historical cost estimating experience are represented.

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As Americans witness the slow dissociation of the military from the civilian public, the need for a strong design initiative within military installations proves as applicable and necessary as it has always been. The role of the designer within the military is a longstanding and vigorously debated duty; the superficially disparate natures of the professions separate themselves on the premise of individual superiority, and isolate their fields of expertise from one another. However, the two microcosms retain an identity that may serve traditionally different clientele, but their purposes reflect and complement one another. This notion is best exemplified by the pedagogy often associated with architecture and the military: a community working tirelessly to construct a system best adapted to the public, regularly working with a client who does not have a clear vision of the resolution, but instead relies on the services of both occupations to not only visualize the outcome but to design the process as well. The all contingency of accredited designers within a typical military hierarchy have been tasked with creating a conducive living environment centralized around "the mission". While they have toiled endlessly to produce such a product, the unfortunate reality demonstrates that the weight of schematics has been typically relegated to grandsons of civil engineers and civilians with unrelated degrees and very little experience in a headquarter building hundreds of miles away. Bearing this in mind, the purpose of this thesis is to discover the greater organization of a military base, and to standardize it not according to chance doctrine, but soundly informed and localized knowledge of the surrounding environment. Such a design must be informed by a few key aspects; principally, the macro intention of such a layout must be centralized around "the mission", which in the case of most military bases, resembles a training and living environment conducive to deployment and combat effectiveness. Similarly, the determination of design must be within the scope of economic feasibility, which although quite gratuitous at first glance, is meagerly distributed throughout the separate branches and therein the country. Lastly, the design must have tenacity, as the ebb and flow of active duty populations produce an arbitrary fluctuation, but the life expectancy of such buildings is often projected within the fifty- to sixty-year time frame. Through careful research, and the benefit of personal interviews with clients who have spent collective centuries in the modern military, a design solution for the improved daily lives and increased combat effectiveness of the American military will serve to discuss the ways in which we can inform the macroevolution of military installations through dissecting the micro. / 0 / SPK / specialcollections@tulane.edu

This thesis presents a framework to conduct a quantity take-off (QTO) and cost estimate within the Building Information Modeling (BIM) Environment. The product of this framework is a model-based cost estimating tool. The framework addresses the cost uncertainty associated with the detailed information defining BIM model element properties. This cost uncertainty is due to the lack of available tools that address detailed QTO and cost estimation using solely a BIM platform. In addition, cost estimators have little experience in leveraging and managing information within semantic-rich BIM models. Unmanaged BIM element parameters are considered a source of uncertainty in a model-based cost estimate, therefore they should be managed and quantified as work items. A model-based system, which assists the estimators to conduct a QTO and cost estimate within the BIM environment, is developed. This system harnesses BIM element parameters to drive work items associated with the parameter’s host element. The system also captures the cost of scope not modeled in the design team’s BIM models. The system consists of four modules 1) establishing estimate requirements, 2) planning and structuring the estimate, 3) quantification and costing, and 4) model-based historical cost data collection. The complete system can produce a project cost estimate based on the 3D BIM Model. This framework is supported by a computation engine built within an existing virtual design and construction (VDC) model review software. The computation engine supports BIM authoring and reviewing BIM data. The Framework’s quantification and costing module was compared to existing methods in a case study. The outcomes of the model-based system demonstrated improved cost estimate accuracy in comparison to the BIM QTO method and improved speed compared to the traditional methods. The framework provides a systematic workflow for conducting a detailed cost estimate leveraging the parameters stored in the BIM models.

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This thesis describes a feasibility study into the use of a new method of timber construction developed in New Zealand. This new method combines the use of an engineered wood product (Laminated Veneer Lumber) and post-tensioned ductile connections. Three case study buildings are presented in concrete, steel and timber all representing current design and construction practice. A fourth building, referred to as the “timber plus” structure, is also considered with the addition of timber architectural components. The case study timber building consists of two lateral resisting systems. In one direction post-tensioned LVL moment resisting frames are used, with post-tensioned cantilever walls in the orthogonal direction. Timber-concrete composite floor units are also used. The method of design and detailing of the timber building is shown with member sizes being found to be comparable to that of the concrete structure. Sub-assembly testing is performed on some key connections with excellent results. Construction time is evaluated and compared to the concrete structure with similar construction times being achieved. Finally the costs of the case study buildings are calculated and compared. The costing found the four options to be similar in price with the Timber and Timber plus buildings showing only a 6% and 11% increase in total cost respectively.

Recently, construction cost variations have been larger and less predictable. These variations are apparent in trends of indices such as Engineering News Record (ENR) Construction Cost Index (CCI) and National Highway Construction Cost Index (NHCCI). These variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owner organizations. Cost variation has become a major concern in all industry sectors, such as infrastructure, heavy industrial, light industrial, and building. The major problem is that construction cost is subject to significant variations that are difficult to forecast. The objectives of this dissertation are to identify the leading indicators of CCI and NHCCI from existing macroeconomic, energy and construction market variables and create appropriate models to use the information in past values of CCI and NHCCI and their leading indicators in order to forecast CCI and NHCCI more accurately than existing CCI and NHCCI forecasting models. A statistical approach based on multivariate time series analysis is used as the main research approach. The first step is to identify leading indicators of construction cost variations. A pool of 16 candidate (potential) leading indicators is initially selected based on a comprehensive literature review about construction cost variations. Then, the leading indicators of CCI are identified from the pool of candidate leading indicators using empirical tests including correlation tests, unit root tests, and Granger causality tests. The identified leading indicators represent the macroeconomic and construction market context in which the construction cost is changing. Based on the results of statistical tests, several multivariate time series models are created and compared with existing models for forecasting CCI. These models take advantage of contextual information about macroeconomic condition, energy price and construction market for forecasting CCI accurately. These multivariate time series models are rigorously diagnosed using statistical tests including Breusch-Godfrey serial correlation Lagrange multiplier tests and Autoregressive conditional heteroskedasticity (ARCH) tests. They are also compared with each other and other existing models. Comparison is based on two typical error measures: out-of-sample mean absolute prediction error and out-of-sample mean squared error. Based on the unit root tests and Granger causality tests, consumer price index, crude oil price, producer price index, housing starts and building permits are selected as leading indicators of CCI. In other words, past values of these variables contain information that is useful for forecasting CCI. Based on the results of cointegration tests, Vector Error Correction (VEC) models are created as proper multivariate time series models to forecast CCI. Our results show that the multivariate time series model including CCI and crude oil price pass diagnostic tests successfully. It is also more accurate than existing models for forecasting CCI in terms of out-of-sample mean absolute prediction error and out-of-sample mean square error. The predictability of the multivariate time series modeling for forecasting CCI is also evaluated using stochastically simulated data (Simulated CCI and crude oil price). First, 50 paths of crude oil price are created using Geometric Brownian Motion (GBM). Then, 50 paths of CCI are created using Gaussian Process that is considering the relationship between CCI and crude oil price over time. Finally, 50 multivariate and univariate time series models are created using the simulated data and the predictability of univariate and multivariate time series models are compared. The results show that the multivariate modeling is more accurate than univariate modeling for forecasting simulated CCI. The sensitivity of the models to inputs is also examined by adding errors to the simulated data and conducting sensitivity analysis. The proposed approach is also implemented for identifying the leading indicators of NHCCI from the pool of candidate leading indicators and creating appropriate multivariate forecasting models that use the information in past values of NHCCI and its leading indicators. Based on the unit root tests and Granger causality tests, crude oil price and average hourly earnings in the construction industry are selected as leading indicators of NHCCI. In other words, past values of these variables contain information that is useful for forecasting NHCCI. Based on the results of cointegration tests, Vector Error Correction (VEC) models are created as the proper multivariate time series models to forecast NHCCI. The results show that the VEC model including NHCCI and crude oil price, and the VEC model including NHCCI, crude oil price, and average hourly earnings pass diagnostic tests. These VEC models are also more accurate than the univariate models for forecasting NHCCI in terms of out-of-sample prediction error and out-of-sample mean square error. The findings of this dissertation contribute to the body of knowledge in construction cost forecasting by rigorous identification of the leading indicators of construction cost variations and creation of multivariate time series models that are more accurate than the existing models for forecasting construction cost variations. It is expected that proposed forecasting models enhance the theory and practice of construction cost forecasting and help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects.

Presently, owners are interested in evaluating the feasibility of investing in the construction of new building projects based on cost and time constraints. They need to therefore have an idea about the project construction costs, the time required to finish construction of a project in its conceptual phase, and about the implementation of feasibility study. Because due to associated risks, construction cost estimates and schedules are vital to any project. The research’s objective is to develop a methodology that can be used to create an integrated computer model that helps owners and designers generate construction cost estimates, and derive the baseline schedule for any proposed building project at its feasibility stage. All the relevant data used within the model is collected from the literature and is stored in comprehensive databases designed for this purpose. The data is based on 2011 RSMeans publications and consist of around 4,000 previously constructed projects. The model is developed in a Microsoft environment using Microsoft Excel 2007 and Microsoft Project 2007. This model uses deterministic and stochastic approaches to execute all necessary calculations for the conceptual cost estimate and baseline schedule. A deterministic approach relies on realistic data while a stochastic one relies on incorporating the uncertainty and risk available in calculating the cost and duration of any construction building project. The model is user friendly, flexible and executes all the necessary calculations quickly. The successful development of the model would help owners and investors identify the cost and baseline schedule of proposed projects at the early stages of the project life, so that they have an idea of the budget required for construction and the time needed to recover their investment.

Thesis. 1976. M.Arch.--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography: leaves 181-185. / M.Arch.

A proper understanding of the local construction market is essential for making appropriate project budgeting and planning decisions. State highway agencies typically use highway construction cost indexes (HCCIs) to understand the current market conditions. In the U.S. highway construction industry, the Federal Highway Administration (FHWA) pioneered the concept of a HCCI as an indicator of the national construction market. State Departments of Transportation (DOT) also started developing their state level HCCIs to better represent their state level construction markets. But, some state DOTs noted the lack of guidance to develop and update their HCCIs. This paper summarizes literature review and nationwide questionnaire survey results to identify the current practices of calculating and using HCCIs. There are two methods to generate basket of construction items for HCCI calculation: a) categorized market basket and b) item level market basket. The Fisher index is the most popular indexing formula among the state DOTs and is also recommended by the FHWA and International Monetary Fund (IMF). Despite many potential users of HCCIs, the current use of HCCIs is very limited in state DOTs.