M. Tech. Quantity Surveying (Structured) / The aim of this research is to identify the major cost overrun factors in the construction sector of the Free State Province of South Africa and the effective remedial cost control measures, generate and recommend possible solutions. The primary objective of the research is to identify the major causes of cost overrun in the construction sector of the Free State Province of South Africa and the related effective remedial cost control measures.

Thesis (B.Sc.)-University of Natal, 1998.

The purpose of this thesis was to acquaint the reader on the nature of the uncertainty present in construction price forecasting and to introduce an environment that has the ability to integrate these uncertainties with greater consistency than that possessed by available price models. The objective of this thesis was to establish that uncertainty can be explicitly treated in price forecasting models. This would have two benefits to concerned parties. Firstly, the effectiveness of price forecasts could be improved as provision could be made for any uncertain variable. This will be of great benefit to the client, as a more accurate assessment of the building process could be established at an earlier stage of the design process. Secondly, the price forecast will be more useful to quantity surveyors, architects and clients, as it would 'quantify' the extent of the uncertainty which could be provided for in a more meaningful manner. In order to establish that existing price models do not deal with the uncertainty present at the time of forecasting, the price models used by practitioners were evaluated against the different types of uncertainty found at the different stages of the price forecasting process. Once this had been established, eight techniques that have the ability to treat various forms of uncertainty, were presented. After analysing the techniques abilities to cope with the uncertainties associated with price forecasting, it was established that certain of these techniques do have the ability, and are suitable, to be incorporated into the price forecasting process. From the results of a questionnaire survey conducted on quantity surveying offices in South Africa, it was found that the price models used by practitioners do not take uncertainty into account, and have in fact, the potential for uncertainty inducement. Some of the uncertainty found to be present in the preparation of a construction price forecast include the lack or incompleteness of design information, the uncertainty in the communication of design information, the variability in the data used by quantity surveyors and, the uncertainty in the choice of price model during the different stages of the design process. As a possible solution to the problem of uncertainty, an expert system environment, utilising a three-dimensional classification of uncertainty, has been proposed. It has been proved that this environment has the ability to cater for the uncertainty associated with the price forecasting process, as well as having the attribute of providing the user with the reasoning behind the logic that the expert system has followed, a characteristic not possible with the traditional forms of price models. From the findings of this thesis, it can be concluded that the methods of price modelling used by quantity surveying practitioners, are unable to take uncertainty into account effectively. It can also be concluded that an expert system environment has the ability to handle the different forms of uncertainty found at the various stages of construction design. The proposed model is conceptual in nature and has not been tested in practice. It is therefore recommended that further research be carried out in this field, with the aim of producing a construction price forecasting expert system which utilises the proposed three-dimensional classification of uncertainty.

by Keung Wing-fai, Frederick. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 107-111). / ABSTRACT --- p.i / ACKNOWLEDGMENT --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.viii / LIST OF FIGURE --- p.ix / CHAPTER / Chapter I --- INTRODUCTION TO STUDY --- p.1 / State of Art --- p.1 / Objectives of the Study --- p.4 / Methodology --- p.4 / A Model for study --- p.4 / Deductive Method --- p.6 / Inductive Method --- p.8 / Framework of Study --- p.8 / Chapter II --- QUANTITY SURVEYING AS A SERVICE --- p.10 / Introduction --- p.10 / Quantity Surveying in the Surveying Profession --- p.11 / Land Surveying --- p.13 / Quantity Surveying --- p.13 / General Practice Surveying --- p.13 / Building Surveying --- p.14 / Quantity Surveying in the Construction Industry --- p.14 / The Construction Process --- p.14 / Roles of Key Participants in the Construction Process --- p.17 / Relationship of Key Participants in the Construction Process --- p.18 / Principal Services of the Quantity Surveying Profession --- p.21 / Preliminary Cost Advice --- p.21 / Cost Planning --- p.22 / Contracting Methods --- p.22 / Tendering --- p.23 / Valuation of Construction Work --- p.24 / Project Management --- p.24 / Chapter III --- DEVELOPMENT OF THE QUANTITY SURVEYING PROFESSION --- p.25 / Life Cycle Analysis of the Profession --- p.25 / Concept of Product Life Cycle --- p.25 / Concept of Profession --- p.27 / Noel McDonagh and Professor Peter Brandon's Framework --- p.27 / Profession Life Cycle of Quantity Surveying Applications (Services) --- p.28 / Profession Life Cycle of Quantity Surveying Skills --- p.32 / Profession Life Cycle of Quantity Surveying Knowledge --- p.35 / Future Development of the Quantity Surveying Profession --- p.36 / Application of the Boston Consulting Group (BCG) Model to the Quantity Surveying Profession --- p.36 / Application of Ansoff's Product/Market Expansion Grid to the Formulation of Development Strategies for the Quantity Surveying Profession --- p.39 / Sequence of Development of New Services --- p.42 / Chapter IV --- ENVIRONMENTAL ANALYSIS OF THE QUANTITY SURVEYING PROFESSION IN HONG KONG --- p.45 / Economic Environment --- p.45 / Economic Background of Hong Kong --- p.45 / Construction Industry in Hong Kong --- p.47 / Estimation of Private Sector Quantity Surveying Market Size and Profitability of the Quantity Surveying Profession in Hong Kong --- p.48 / Competitive (Immediate) Environment --- p.49 / Competitors --- p.50 / Potential Entrants --- p.52 / Substitutes --- p.53 / Buyers --- p.54 / Suppliers --- p.56 / Conclusions --- p.56 / Chapter V --- EXPLORATION OF OPPORTUNITIES FOR QUANTITY SURVEYING PROFESSION IN THE PEOPLE'S REPUBLIC OF CHINA --- p.57 / Introduction --- p.57 / Major Factors Affecting Foreign Investment in China --- p.58 / Special Economic Zone --- p.61 / Professional Design Services in China --- p.62 / The Role of Quantity Surveyors in the PRC --- p.63 / Chapter VI --- STRATEGIC CHOICES TO QUANTITY SURVEYING CONSULTANCY FIRMS IN HONG KONG --- p.65 / Segmentation of Market --- p.65 / Market Segmentation by Client Types --- p.66 / Market Segmentation by Work Types --- p.70 / Opportunities and Threats --- p.71 / Levels of Strategies --- p.72 / Literature Review on Strategies --- p.73 / Strategic Directions for Private Sector Quantity Surveying Consultant Firms in Hong Kong --- p.76 / Chapter VII --- RESULTS OF QUESTIONNAIRE TO LOCAL QUANTITY SURVEYING CONSULTANCY FIRMS --- p.87 / Introduction --- p.87 / Collective Results --- p.89 / Characteristic of Firms --- p.89 / Carrying Out of Corporate Planning --- p.90 / Jobs-in-hand and Income Classification --- p.92 / Jobs-in-hand classified according to Work Types --- p.92 / Jobs-in-hand classified according to Client Types --- p.93 / Income classified according to Service Types --- p.93 / Strategic Directions for the Hong Kong Office --- p.94 / Relative Importance of Future Clients --- p.98 / Chapter VIII --- CONCLUSIONS --- p.100 / Strategic Direction of The Quantity Surveying Profession as a Whole in Hong Kong --- p.100 / Strategic Directions of Individual Quantity Surveying Practices in Hong Kong --- p.102 / Summary --- p.105 / REFERENCES --- p.107 / BIBLIOGRAPHY --- p.110 / APPENDIX / Chapter 1. --- "Questionnaire on ""Strategic Management of Quantity Surveying firms in Hong Kong"""

by Asano Masashi. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 97-98). / ABSTRACT --- p.ii / TABLE OFCONTENTS --- p.iii / LIST OF ILLUSTRATIONS --- p.iv / LIST OF TABLES --- p.v / ACKNOWLEDGEMENTS --- p.vi / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- METHODOLOGY --- p.7 / Chapter III. --- PROFIT MARGIN ANALYSIS --- p.22 / Chapter IV. --- SENSITIVITY ANALYSIS --- p.39 / Chapter V. --- COST ESTIMATE ANALYSIS --- p.48 / Chapter VI. --- INDUSTRY PROFITABILITY ANALYSIS --- p.76 / Chapter VII. --- CONCLUSION --- p.87 / APPENDIXES --- p.89 / BIBLIOGRAPHY --- p.97

published_or_final_version / Management Studies / Master / Master of Business Administration

published_or_final_version / abstract / toc / Real Estate and Construction / Master / Master of Science in Construction Project Management

Thesis (MTech (Construction Management))--Cape Peninsula University of Technology, 2017. / The study investigates the predominant factors responsible for increase in the cost of building materials and the effect of this cost increase on housing delivery in Western Cape, South Africa. Sustainable housing is buildings produced to meet the present housing needs of people without conceding the ability of the future generation to meet their future needs. However, a significant increase in the cost of building materials has been a major constraint to the delivery of sustainable housings, as made evident in the literature, leading to project cost and time overruns or even project abandonment. However, building materials consume up to 65% of the total cost of construction. This factor on cost has, over the years, threatened the ability of the construction industry to deliver projects within budgeted cost, at stipulated time, and at satisfactory quality. This prompted the need to proffer solutions to these factors identified which are causing increases in the cost of building materials towards sustainable housing delivery in Western Cape. Based on this research study, housing is termed to be sustainable when it is available and affordable for the masses timely and at quality expected. The research study adopted a mixed methodological approach, involving the use of semi-structured qualitative interviews and closed-ended quantitative questionnaires administered to construction stakeholders (architects, quantity surveyors, engineers, construction managers, project managers, site supervisors and material suppliers) in the Western Cape Province of South Africa. SPSS version 24 software was used for analysing the quantitative data collected and ‘content analysis’ method was used to analyse the information collected through the qualitative interviews. The findings revealed that the major factors responsible for increasing the cost of building materials are inflation, wastages of building materials by labourers, cost of transportation and distribution of labour, design changes, client contribution to design change and change in government policies and regulation. Moreover, the research showed that fluctuation in the cost of construction and high maintenance costs due to poor workmanship also impact the cost increase of building materials for housing delivery. In addition, research findings affirmed that for optimum materials usage for the enhancement of sustainable construction, the following criteria should be considered in the selection of building materials: maintenance cost, energy consumption and maintainability. The adoption of these findings by construction stakeholders in the South African construction industry would enhance the delivery of affordable housing at reduced cost, at the required time and at the expected quality. Therefore, an adequate implementation of the framework presented in this study will enhance sustainable housing delivery.

M.Ing. (Engineering Management) / Capital intensive engineering projects involve the flow of large sums of money over the project life. During the pretender phase of the project, estimates of the forecast cash flow and associate return on investment are made based on certain assumptions which at the time are, at best, only informed guesses. As the project progresses, the uncertainty surrounding the magnitude and the timing of these cash flows and rate of return, diminishes. By recognising the time value of money and the importance of the synthesis of time and cash flow as well as the interdependence of project activities, it is desirable to obtain an estimate of the PROBABLE outcome of the return on investment~ should the bid be accepted. This outcome can only be 'guessed' at by means of a probabilistic analysis of the parameters that go to make up the nett cash flow. This study is an attempt to find a model suitable for use in the construction industry which accurately describes the construction process, and presents an overall analysis of the variation in the rate of return as a result of the probabilistic nature of the original parameters. Various models were investigated. All were found suitable under limited conditions.

This thesis deals with the potential application of neural networks technology to construction cost estimating problems. This is done by developing neural networks applications for a number of case studies constructed from the historical cost data of actual construction projects. Parameter-based cost estimating applications, which require the application of analysis and prediction techniques to the cost data of a given estimating problem, were chosen as the major field of investigating the implementation of neural networks in this thesis. The objective of this thesis is to investigate whether or not neural network computing technology should be considered as a viable alternative in cost estimating applications by comparing it with conventional parameter-based analysis tools or predictive methodologies currently used to estimate construction costs. Both methodologies, parametric estimating and neural networks, use a parameter-based approach in modeling cost. However, the computational techniques used by the two methodologies to analyze cost data and produce results are significantly different. Four case studies were the subject of comparison. The four case studies were compiled from the records of two construction companies and focus mainly on two areas: (1) Industrial projects and (2) Bridge construction. / Master of Science