Bina yapımında simülasyon yaklaşımıyla maliyet tahmini
Cost estimating with simulation approach in building construction
Yazar: NİLÜFER YAYLAGÜL
Danışman: PROF. DR. HEYECAN GİRİTLİ
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Bina Yapım Yönetimi Bilim Dalı
Derece: Yüksek Lisans, 1994
Konu: Mimarlık = Architecture
Dizin: Benzetim = Simulation ; Bina üretimi = Building production ; Maliyet modeli = Cost model ; Maliyet tahmini = Cost estimation

Özet:
Bina maliyetini etkileyen önemli kararlar, büyük ölçüde ön karar evresinde alınmaktadır. Bu düşünceden yola çıkılarak, risk faktörünü de hesaba katan, ön karar evresinde kullanılabilecek bir mali yet tahmin yaklaşımı oluşturulmaya çalışılmıştır. Girişte, çalışmanın amacı ve konunun işlenişi belirlendikten sonra, ikinci bölümde maliyet tahmini konusu irdelenmiş ve tahminin doğruluğunu etkileyen etmenler incelenmiştir. Üçüncü bölümde; genel anlamda maliyet tahmin modelleri ne yer verilmiş ve araştırmada kullanabileceğimiz en uygun maliyet modeli saptanmaya çalışılmıştır. Dördüncü bölümde; ikinci bölümde amacımıza uygun olduğuna karar verdiğimiz simülasyon yaklaşımının kullanma amacı ve yön temi hakkında bilgi verilmiştir. Beşinci bölümde; Monte Carlo Simülasyonu yardımıyla, fonksiyonel eleman düzeyinde analiz yapılarak, konut projelerinde ön karar evresinde kullanılabilecek bir maliyet tahmini oluşturulmuştur.

Abstract:
Important decisions that affect the building cost are taken in the planning at early stages of a project. Therefore cost estimating has a great importance in the feasibility and preliminary design stages. Regarding this as a starting point, the aim of this thesis is to develop a cost estimate approach that taken into account the factor of risk. Therefore, cost estimation models are examined and due to the factor of risk, the model of simulation is agreed to be the most appropriate estimation model to use. Cost estimating is a process of predicting the most likely cost of a construction project. The accuracy of estimates is very important for decision maker. The accuracy of estimates produced is generally proportional to the amount of time devoted to the activity, the applied level of detail and the estimating technique used together with the peculiarities associated with specific projects such as complexity and accuracy of tender documentation. There are numerous reasons why actual cost differs from estimated cost, many of which are completely unrelated to the accuracy of the original estimate, so much so that one estimate may be nearer to actual cost than another more accurate estimate. Estimating techniques are unlikely to produce completely accurate estimates. The main factors which give rise to inaccuracies are outlined as follows: - Human errors - Expertise - Prediction - Data - Time The quality of construction price forecasts is ameasure of the satisfaction obtained by the purchaser of the forecasts. Five viprimary factors determine the quality of forecasts. These are: 1. The nature of the target - The type of project - Other project characteristics - Geographical location - The contract procurement system - The nature of the competition - The prevailing economic climate 2. Levels of information 3. The forecasting technique used 4. The use of feedback 5. The ability of the forecaster - Quality levels - Attributes of forecasters - Acquisition and application of expertise. COST ESTIMATING MODELS Cost estimating models should be used at all stages of the design and construction process. Cost estimating models are usually used both for estimating the probability of a budget over run and for determining the amount of contingency funds required to assure that the project costs remain within the budget. Aims of modelling can be listed as follows; 1. To give confidence to the client with regard to the expected cost of his project. 2. To allow the quick development of a representation of the building in such a way that its cost can be tested and analysed. 3. To establish a suitable system for advising the designer on cost that is compatible with his own build up of the design. 4. To establish a link between the cost control of design and the manner in which cost are generated and controlled on site. Cost models can be classified in different ways. One of them is the following classification; I. Traditional cost models a) Single price estimating models - The unit method - The cube method - The superficial area method vu- The storey enclosure method b) Elemental estimating c) Operational estimating d) Resource related methods II. Contemporary Cost Models a) Causal or empirical models b) Regression models c) Simulation models The descriptive primitives are intended to give some formal basis to the way in which alternative approaches to cost modelling might be classified. The descriptive primitives are categorized under the following nine headings: proposal. applied 1. Relevance - whether it relates to a specific design 2. Units - the units of measurement 3. Cost/price - how the model is intended to be used 4. Approach - the level at which modelling is applied 5. Time-point - when during the design process it is 6. Model - a general classification of technique 7. Technique - the specific classification 8. Assumptions - whether they can they be accessed or not 9. Uncertainty - how it is treated. These nine criteria are used to classify alternative approaches to cost modelling and are described in more detail below: 1. Relevance - Specific - Non-specific 2. Units - Abstract - Finished work - As-Built 3. Cost/Price - Cost - Price 4. Approach - Micro - Macro 5. Time-point - Feasibility - Sketch design - Detail design - Tender vui- Throughout - Non-construction 6. Model - Simulation - Generation - Optimization 7. Technique - Dynamic Programming - Expert Systems - Functional Dependency - Linear Programming - Manual - Monte Carlo Simulation - Networks - Parametric Modelling - Probability analysis - Regression analysis 8. Assumptions - Explicit - Implicit 9. Uncertainty - Deterministic - Stochastic factors, the cost Choice of the appropriate model depends on the following - purpose of the cost predcition - amount and quality of the data which is used to predict - characteristic of the work which is predicted - cost of the modeling - time dedicated to the cost estimation. As a result, regarding the aim of this study together with the factors affecting the choice of a model, Monte Carlo simulation is agreed to be one of the most appropriate models for this study. SIMULATION: The construction industry is subject to more risk and uncertainty than probably any other industry. Buildings tend to be bespoke and each new project involves new design and construction problems that have to be overcome. The environment within decision-making takes place can be divided into three pats: IX. certainty. risk. uncertainty. Certainty does not happen very often in the construction industry. An important source of bad decisions is fairly often illusions of certainty. There is a difference between risk and uncertainty. A decision made under risk when a decision-maker can assess, either intuitively or rationally, the probability of a particular event occuring, the probabilities of the event being based upon historical data or "experience". Uncertainty, by contrast, might be defined as a situation in which there are no historic data or history relating to the situation being considered by the quantity surveyor. The construction industry uses the term risk to encompass both risk and uncertainty. It is important to distinguish the sources of risk form their effects. The effects of risk are:. failure to keep within the cost budget, forecast, estimate, tender. failure to keep within the time stipulated for the design construction and commissioning. failure to meet the required technical standards for quality, function, fitness for purpose, safety and environment preservation. The Risk Analysis Techniques can be classified as follows:. Sensitivity Analysis. Decision Tree Analysis. Probability Analysis (Simulation) Simulation is simply a means of creating a typical life-history of the system (e.g. total building, the production process, maintenance, costs-in-use) and activities under given conditions, working out step by step what happens during each unit of life of the system. In order to do this we need to know the detailed characteristics and operation of the system and its relevant measures. Simulation is a word which is in common use today. The term simulation describes a wealth of varied and useful techniques,all connected with the mimicking of the rules of a model of some kind. Simulation techniques are used extensively in industry. Perhaps the most difficult forecast to make with regard to buildings is the cost of running and maintaining the property, not only because there are so many factors affecting the way the building performans (maintenance strategy, standard of use, design detailing workman-ship, etc) but also because of the long period of time over which the prediction must be made. To illustrate the ability of Monte Carlo Simulation to assist us in decision - making by giving us more information on the range of possible future costs. Simulation models attempt to reflect the risk associated with duration estimates by incorporating the uncertainties surrounding the variables used. Estimators have long been aware of construction risks but traditional methods of including them in the calculations have tended to obscure the issues. The approach described here attempts to capture the estimators perception of risk in a realistic way be eliminating the need for the estimator to make one best estimate for each variable. In the construction of a model for Monte Carlo Simulation, there are two special requirements. The first is that the model be constructed in such a way that the main variables are independent. The second, and less important, requirement is knowledge of the probability distribution of each variable. Monte Carlo Simulation generates mean unit price rates for each elemental category in the cost plan for the proposed building. These rates are taken from probability distributions with the same statistical properties, that is probability density functions, as those which characterise the original sample data from which the mean unit price rates were estimated. The hypothetical rates are then used to build up a total price forecast for the proposed building. If this exercise is repeated a sufficiently large number of times, it will be possible to obtain a picture of the probability density function which characterises the total price, and so to identify the most likely total price. There are several steps to make the Monte Carlo Analysis: 1. Data collection 2. Determine the probability distribution 3. Generate the random variables 4. Simulations 5. Interpretation of output XIThe output of Monte Carlo simulation is given in the form of probability distribution (histogram) and cumulative probability distributions. In the fifth chapter, a model for housing projects has been developped by using the Monte Carlo Simulation. A sample was chosen from the data that consisted of 30 housing projects. It was assumed that cost of each element was normally distributed. A probability distributions or histogram and cumulative probability distributions were drawn for each of the functional elements. It is then necessary to generate the random numbers for the simulation. The functional element's unit rates were estimated by use of random variables and distributions. This rates were then used to build up a total unit rates for housing projects. This exercise was repeated 100 times. As result, histogram and cumulative probability distributions were drawn for 100 simulations. The result of distributions are as follows: Unit Rates Probability 425.000 TL- 637.500 TL 1% (1/100) 637.500 TL- 850.000 TL 18% (18/100) 850.000 TL - 1.062.500 TL 55% (55/100) 1.062.500 TL - 1.2275.000 TL 26% (26/100) That results could used in the feasibility and preliminary design stages for housing projects. But, we should'nt forget that this cost will be estimated approximately in the frame of some acceptances.