Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.
Civil engineering presents a specific branch of industry from all aspects. The main reason for this lies in specific features of construction objects as well as the conditions for their realisation. Another specific aspect of realisation of construction projects is that the realisation process involves a large number of participants with different roles. The key role in the realisation of construction processes is certainly played by an investor, who is at the same time the initiator of realisation of a construction project, whose main goal is to choose a reliable contractor who can guarantee the fulfilment of set requirements (costs, time, and quality).
When choosing a constructor, a dominant parameter is the offered cost of realisation, which implies that it is necessary to carry out an adequate estimation of construction costs. The question that arises is which costs, that is, which price, should be the subject of estimation. Gunner and Skitmore [
Some authors, however, Aibinu and Pasco [
According to everything mentioned above, the estimation of costs within this research was carried out based on the realisation value offered by the contractor.
Further on, there are two levels of estimation of potential works from the perspective of a contractor, which precede the realisation of contracting, and those are conceptual (rough) and preliminary (detailed) estimation [
It was noted that the dominant parameter for choosing the most favourable contractor is the offered price. However, the proposed time for realisation of works in question should not be neglected either. The main problem of estimation of duration of works in the conceptual phase is the fact that the potential contractor does not possess realisation plans, which implies the application of estimation methods which provide satisfactory accuracy based on data available at a time. It is a common case for an investor to limit the maximum duration of construction in the tender conditions.
Having in mind that it is necessary to carry out simultaneous estimation of costs and duration of the construction process, the fact that the research confirmed that the costs of significant position of works are at the same time of temporal significance is highly important [
It was mentioned that conceptual estimation requires application of simpler and specialised models with an acceptable accuracy of estimation. One of the possible approaches is the application of artificial intelligence (artificial neural networks (ANNs), support vector machine (SVM), etc.). The basic precondition for the application of this approach is the forming of an adequate base of historical data on similar construction projects previously realised.
The first scientific article related to the application of ANNs in construction industry was published by Adeli in the Microcomputers in Civil Engineering magazine [
Cost estimation by using ANNs and SVM is often represented in the literature, for example, estimation of construction costs of residential and/or residentialcommercial facilities [
Kong et al. [
Kim et al. [
Since the topic of the research is the estimation of costs and duration of construction of urban roads, the following text will contain research carried out on similar types of constructions. Wang et al. [
Hegazy and Ayed [
Estimation of duration of the construction of buildings by using ANNs and SVM is not present in the literature, as it is the case with estimation of costs. Attal [
Within the research carried out, gathering of data and data analysis were performed, followed by the preparation of data for the needs of model formation, as well as the final forming of models and their comparative analysis.
Gathering of data and forming of the data base on realised construction projects of reconstruction and/or construction of urban roads were carried out on the territory of the city of Novi Sad, the Republic of Serbia. All the projects were funded by the same investor in the period between January 2005 and December 2012. All the projects solely relate to the realisation of construction works based on completed projects and technical documentation. Having in mind everything mentioned above, uniform tender documentation, that is, the bill of works which makes its integral part, served as the main source of information.
The sample comprises 198 contracted and realised construction projects. However, not all of the projects were included in the analysis carried out later on, owing it to the fact that certain projects, 32 of them in total, include realisation of works on relocation of installations (sewerage, water, gas, etc.), green spaces landscaping, street lighting, and construction of supporting facilities on the roads (smaller bridges, culverts, etc.), which are excluded from further analysis.
The total number of realised projects included in the further analysis amounts to 166 projects of basic construction works and/or reconstruction of urban roads. As it has been already noted, all projects were realised for the same investor. Consequently, the tender documentation is uniform, which is also the case with the distribution of works within the bill of works, dividing them into preparation works, earthworks, works on construction of pavement and landscaping, drainage works, works on construction of traffic signals, and other works.
The analysis of the share of costs of the mentioned work groups in the total offered price of realisation was carried out. This confirmed that the works on roadway structure and landscaping have the biggest impact on the total price, ranging within the interval from 22.24% to 100% (only two cases where only these two types of works were planned). Earlier research included similar analysis, but only for projects realised by the same contractor [
Table
Mean values of the percentage of works groups in the total offered value.
Number  Group of works  Mean value of the percentage of works groups in the total offered price [%] 

(1)  Roadway construction and landscaping works  68 
(2)  Earthworks  14 
(3)  Preparation works  12 
(4)  Other works  3 
(5)  Drainage works  2 
(6)  Works on traffic signals installation  1 
Considering the fact that for 63.86% of analysed projects the share of the total price ranges within the interval between ±15% and 80% of the total offered value, it can be stated that the works on roadway construction and landscaping are costsignificant according to “Pareto” distribution, that is, distribution of 20/80. This is an additional reason why these works play the major role when defining an estimation model. Realisation of works on roadway construction and landscaping relates to usage of basic materials, such as crushed stone (different fractions), curbs, asphalt base layer, asphalt surface layer, and concrete prefabricated elements for paving.
Duration of realisation is offered in the form of the total number of days and there is no way in which it can be claimed with certainty how much time is needed for the realisation of each group of works individually. For this reason, classification of projects was carried out based solely on the total amount of time offered for the realisation of all planned works. Table
Number of projects according to the offered number of days for realisation.
Number  Offered number of days  Number of projects  Percentage share in the data base [%] 

(1)  Up to 20  50  30.12 
(2)  21 to 30  31  18.67 
(3)  31 to 40  24  14.46 
(4)  41 to 50  26  15.66 
(5)  51 to 60  14  8.43 
(6)  61 to 70  5  3.01 
(7)  71 to 80  7  4.22 
(8)  81 to 90  4  2.41 
(9)  Above 90  5  3.01 
Estimation of costs, as well as all the other estimations such as duration of realisation, involves the engagement of resources. According to the literature, estimation of costs ranges between 0.25% and 1% of the total investment value [
The primary purpose of forming of an estimation model is to perform the most accurate estimation possible in the shortest interval of time possible, with the minimum engagement of resources, all of it based on the data available at the time of estimation. Since the contracts in question are the socalled “build” contracts, an integral part of tender documentation is the bill of works, or more precisely the amount of works planned in the projecttechnical documentation. Estimations of costs based on amounts and unit prices are the most accurate ones but require a great amount of time and need to be applied in preliminary (detailed) estimation, which precedes directly the signing of a contract. However, conceptual (rough) estimation which results in the total costs of realisation requires simpler and faster methods of estimation. With the aim of defining a method featuring such performances, a formerly presented analysis of significance and impact of groups of works on the total price, both on total costs and on realisation time, was carried out.
The works on roadway construction and landscaping, as the most important group of works, were considered in more detail in comparison with other groups of works; that is, they were assigned greater significance. Having in mind the characteristics of works performed within this group, a large amount of material necessary for their realisation being one of them, the input parameters for creating of this model are the amounts of material necessary for their realisation (Table
Despite the fact that the mean percentage share of roadway construction and landscaping works amounts to 68%, the share of the remaining groups of works in the total offered value should not be neglected. The biggest share in the total offered value for realisation of the remaining groups of works belongs to earthworks and preparation works, whereas traffic signals, other works, and drainage contribute with a considerably lower percentage (Table
Inclusion of the mentioned works in further analysis was carried out based on the number of planned positions of works for each individual bid project in relation to a possible number of positions of works (according to a universal list issued by the investor) in groups of works, directly through percentage share (Table
Moreover, it was noticed that it is possible to classify realised works based on the location they were supposed to be realised on. For that purpose, realisation of works was divided into two zones: zone 1, realisation of works in the city centre, and zone 2, realisation of works in the suburbs (Table
Since the research carried out relates to the financial aspect of realisation of construction projects, it is necessary to perform revalorisation, in order for the data to be comparable and applicable to forming of an estimation model by using artificial intelligence. By using the revalorisation process, defining of difference is achieved, that is, the increase or decrease of offered values for the realisation of works in relation to the moment in which the estimation of realisation costs for future projects will be made, by applying the model. In other words, by applying the revalorisation process, the changes in the prices defined on the base date in relation to the current date will be defined. Base date is the date on which the contracted price was formed (i.e., giving an offer), whereas the current date presents the date on which the revalorisation is carried out.
Revalorisation is made based on the index of general retail prices growth in the Republic of Serbia, where the % increase in the period between February 2005 and July 2012 amounted to 95.47%, which is close to the mean value of increase of 89.91%, obtained on the basis of the values of unit prices from two final offers. After the completed revalorisation, the contracted (offered) values of realised works are directly comparable; that is, they can be classified based on the total offered revalorised value for the realisation of works (Table
Number of projects depending on the offered revalorised price for realisation of works.
Number  Offered price for realisation [RSD] 
Number of projects  Percentage share in data base [%] 

(1)  Up to 5,000,000  32  19.2 
(2)  From 5,000,000 to 10,000,000  28  16.87 
(3)  From 10,000,000 to 20,000,000  24  14.46 
(4)  From 20,000,000 to 30,000,000  19  11.45 
(5)  From 30,000.000 to 40,000,000  13  7.83 
(6)  From 40,000,000 to 60,000,000  8  4.82 
(7)  From 60,000,000 to 100,000,000  21  12.65 
(8)  From 100,000,000 to 200,000,000  13  7.83 
(9)  Over 200,000,000  8  4.82 
Inputs into models.
Number  Description of input data  Data type  Unit of measurement  Min  Max  Mean value 

Input 1  Amount of crushed stone  Numerical  m^{3}  0,00  16,070.00  1,694.62 
Input 2  Amount of curbs  Numerical  m^{1}  0,00  14,300.00  1,975.07 
Input 3  Amount of asphalt base layer  Numerical  t  0,00  31,569.00  1,119.61 
Input 4  Amount of asphalt surface layer  Numerical  t  0,00  11,046.00  505.85 
Input 5  Amount concrete prefabricated elements  Numerical  m^{2}  0,00  20,000.00  2,824.85 
Percentage share of wok positions  
Input 6  Preparation works  Numerical  %  0,00  100.00  43.18 
Input 7  Earthworks  Numerical  %  0,00  100.00  48.92 
Input 8  Drainage works  Numerical  %  0,00  93.33  16.63 
Input 9  Traffic signalisation works  Numerical  %  0,00  100.00  28.66 
Input 10  Other works  Numerical  %  0,00  100.00  8.59 
Input 11  Works realisation zone  Discrete    1  2   
Input 12  Project category (values of up to and over 40,000,000)  Discrete    1  2   
Classification of projects according to the total value for the realisation of works can be of great importance when training/testing of formed models for estimation. For that reason, an additional input parameter was introduced, which divides projects into two subsets (values of up to and over 40,000,000 RSD) (Table
Outputs from the models.
Number  Input data description  Data type  Unit of measure  Min  Max  Mean value 

Output 1  Total offered cost of realisation  Numerical  RSD  883,353.01  395,427,276.11  45,705,301.56 
Output 2  Total offered duration of realisation  Numerical  day  5  120  ≈38 
According to the previously defined subject of study, two outputs from the model were planned, the total offered price for realisation and total amount of time offered for the realisation of a project.
The next step in preparing of the data base is the normalisation process. Normalisation of data presents the process of reducing of certain data to the same order of magnitude. What is achieved in this way is for the data to be analysed with the same significance when forming a prediction model, that is, to avoid neglecting of data with a smaller order of magnitude range at the very beginning. This is the main reason why the normalisation is necessary; that is, why it is essential to transform the values into the same range by moving the range borderlines. The normalisation process was carried out for the entire set of 166 analysed projects.
Before the normalisation process is applied, considering the fact that a model based on artificial intelligence will be formed, it is necessary to divide the final set of 166 projects into a set that will be used for the training of a model, as well as the one used for the testing of formed models. Defining of which data subset will be used for the training of a model and which one for its testing, is not entirely based on the random sample method. The comparative analysis was carried out of the number of projects by categories based on the offered revalorised total price as well as the offered time for realisation of all works.
When choosing the projects that belong to the training subset, particular attention was paid to the fact that the minimum and maximum values of all parameters belong to the range of this set. In addition, all groups of projects should be equally present in both sets, according to the offered value and time for realisation. Finally, the testing subset comprised 17 pseudorandomly chosen projects (with mentioned restrictions), whereas the remaining 149 projects constitute the training subset.
The most commonly used forms of data normalisation, being the simplest ones at the same time, are the minmax normalisation and ZeroMean normalisation [
The first step in forming of models for estimation by using ANNs relates to defining of the number of hidden layers. According to Huang and Lipmann [
By choosing the optimal number of neurons, it is necessary to avoid two extreme cases: omission of basic functions (insufficient number of hidden neurons) and overfitting (too many hidden neurons). In order to achieve proper generalisation “power” of an ANN model, it is necessary to apply the crossvalidation procedure, owing it to the fact that good results during the training process do not guarantee proper generalisation “power.” What is meant by generalisation is the “ability” of an ANN model to provide satisfactory results by using data which were not known to the model during the training (validation subset).
For the purpose of the crossvalidation procedure within the training subset, 17 pseudorandomly chosen projects were taken, based on the same principle as within the testing subset, that is, the equal percentage share of projects in terms of value. If there is not too much difference, that is, deviation between estimated and expected values, percentage error (PE) or absolute percentage error (APE), or mean absolute percentage error (MAPE), in all three subsets (training, validation, and testing), it can be considered that this is the actual generalisation power of the formed ANN model, that is, that there is no “overfitting.”
All the models for estimation of costs and duration were formed in the Statistica 12 software package, in which it is possible to define two types of ANN models, MLP (Multilayer Perceptron) and RBF (Radial Basis Function) models. According to Matignon [
Activation function of output neurons is mainly linear when it comes to regression problems. When the activation functions of hidden neurons are concerned, the most commonly used functions are logistic unipolar and sigmoidal bipolar (hyperbolic tangent being the most commonly used one) [
Activation functions of MLP ANN models.
Function  Expression  Explanation  Range 

Identity 

Activation of neurons is directly forwarded as output 



Logistic sigmoid 

“S” curve 



Hyperbolic tangent 

Sigmoid curve similar to logistic function, but featuring better performances because of the symmetry it has. Ideal for MLP ANN models, especially for hidden neurons 

Based on previously defined inputs, outputs, and defined parameters in each iteration, 10.000 ANN MLP models were formed, and one model with the smallest estimation error was chosen. The number of input and output neurons was defined by the number of inputs and outputs, whereas the number of hidden neurons was limited to maximum of 10. A total of 12 ANN models were chosen, 6 of them being normalised by the minmax procedure and the remaining 6 by the
By using the ANN 1 and ANN 2 models, simultaneous estimation of costs and duration was carried out. The ANN 1 model had 12 inputs and 2 outputs, whereas the ANN 2 model was formed by using 11 inputs. Elimination of one input parameter followed the analysis of the influence of input parameters, which showed that the realisation zone (11i) has the smallest influence on the values of the output data. The same principle was applied in forming of ANN 3 and ANN 4 models for the estimation of costs only, as well as the ANN 5 and ANN 6 models for the estimation of time needed for the realisation of works.
Table
ANN models (minmax).
Model  Network  Activation function hidden neurons  Activation function output neurons  MAPE training (cost) [%]  MAPE training (duration) [%]  MAPE testing (cost) [%]  MAPE testing (duration) [%] 

ANN 1  MLP 1242  Tanh  Identity  42.79  31.85  40.54  35.48 
ANN 2  MLP 1162  Logistic  Identity  41.88  31.82  26.97  30.22 
ANN 3  MLP 1261  Tanh  Identity  33.02  /  25.38  / 
ANN 4  MLP 1171  Tanh  Identity  39.16  /  26.88 

ANN 5  MLP 1281  Tanh  Identity 

34.16  /  26.26 
ANN 6  MLP 11101  Logistic  Identity 

33.29  /  35.16 
Forming of the remaining 6 ANN models with data whose normalisation was performed by using the ZeroMean normalisation was carried out in the same way. In this case as well the realisation zone (11i) has the smallest impact on the output values. Table
ANN models (ZeroMean).
Model  Network  Activation function hidden neurons  Activation function output neurons  MAPE training (cost) [%]  MAPE training (duration) [%]  MAPE testing (cost) [%]  MAPE testing (duration) [%] 

ANN 7  MLP 1272  Logistic  Identity  52.16  30.89  37.96  34.23 
ANN 8  MLP 1182  Logistic  Identity  48.04  32.06  42.54  34.20 
ANN 9  MLP 1241  Tanh  Identity  37.49 

20.22 

ANN 10  MLP 1181  Tanh  Identity  40.99 

28.28 

ANN 11  MLP 1281  Tanh  Identity 

32.32 

37.20 
ANN 12  MLP 1151  Tanh  Identity 

33.13 

35.59 
The comparative analysis of presented models clearly shows that the greater accuracy of estimation is achieved by models formed based on the data prepared by the minmax normalisation procedure. The accuracy of estimation of formed models is unsatisfactory, that is, being considerably larger than the desirable ±15% for the costs of construction.
The first step in the forming of models for estimation by using the SVM as well as the ANN models relates to defining of input and output data. In the process of forming of SVM models, the used data had previously been prepared by applying the minmax normalisation, as it was proved that using it results in the greater accuracy in the case of ANN models. Moreover, only the models for separate estimation of costs and duration of construction were formed. The main reason for this lies in the fact that the greater accuracy is achieved by separate estimation, that is, by forming of separate models, which was proved on the ANN models. However, the software package Statistica 12 itself does not provide the option of simultaneous estimation of several parameters by using the SVM. Within the mentioned software package, two functions of error in the forming of SVM models are offered (Table
Functions of error of SVM models.
SVM type  Error function  Minimize subject to 

Type 1 








Type 2 





For Type 1 (epsilonSVM regression) it is necessary to define parameter capacity (
A total of four SVM models were formed, with the same number of input parameters as in the case of ANN models, from the perspective of input parameters SVM 1 = ANN 3 and ANN 9, SVM 2 = ANN 4 and ANN 10, SVM 3 = ANN 5 and ANN 11, and SVM 4 = ANN 6 and ANN 12. The reason for this is the easier comparative analysis of results obtained by using the listed models. Table
SVM models (minmax).
Model 



MAPE training 
MAPE training (duration) [%]  MAPE testing (cost) [%]  MAPE testing (duration) [%] 

SVM 1  20  0.001  0.083  25.28  /  15.47  / 
SVM 2  20  0.001  0.091  23.96  /  7.06  / 
SVM 3  20  0.001  0.083  /  29.21 

24.59 
SVM 4  20  0.001  0.091  /  30.75  /  22.77 
After forming the SVM models, it is evident that they provide greater accuracy of estimation of both costs and duration of projects as well. The shown accuracy of estimation made through the MAPE is not sufficient for the choice of a model, but it is necessary to carry out the analysis of accuracy of estimation for each of the projects separately, especially those from the testing subset. The estimation error is expressed through the PE (percentage error), which is shown in Table
PE for estimation of costs, testing set.
Expected cost [RSD]  PE for the cost for the model testing [%]  

ANN 1  ANN 2  ANN 3  ANN 4  SVM 1  SVM 2  
(1)  2.648.222,52  −102.64  50.14  7.02  −22.16  35.13  29.26 
(2)  3.745.996,06  −135.49  24.81  −64.12  −6.85  10.90  2.95 
(3)  4.316.450,20  −71.75  −3.35  −58.63  −4.19  3.30  −3.69 
(4)  4.406.745,87  −8.84  −28.48  −26.43  43.85  −101.06  −17.16 
(5)  5.894.577,64  −61.60  −56.76  −30.47  −71.95  6.58  −6.68 
(6)  6.228.262,97  108.17  61.10  58.49  70.07  −0.36  11.47 
(7)  7.959.531,14  −8.24  −22.17  −26.71  −45.93  −21.82  −14.67 
(8)  14.402.129,26  −25.13  −10.58  12.93  27.89  −31.66  3.81 
(9)  15.499.081,98  −33.01  −15.10  −25.31  −23.05  6.85  6.93 
(10)  24.298.158,68  −11.78  55.89  −11.72  −5.09  −6.95  2.46 
(11)  29.293.376,43  −6.23  −1.86  −2.24  −18.82  0.25  0.19 
(12)  31.331.583,69  −22.66  −17.92  −9.52  −23.11  −6.58  −3.87 
(13)  48.628.946,36  −12.12  −37.33  4.69  6.09  −4.72  2.68 
(14)  68.428.523,13  −61.68  −25.73  −58.10  −51.68  −13.91  −6.86 
(15)  74.828.211,00  8.16  25.96  7.08  4.65  7.95  4.28 
(16)  121.971.479,98  6.97  11.85  26.16  30.90  2.68  1.90 
(17)  267.333.894,15  −4.63  −9.51  −1.91  −0.62  −2.31  −1.21 
MAPE 






PE for estimation of duration, testing set.
Expected duration [day]  PE for the duration of the model testing subset [%]  

ANN 1  ANN 2  ANN 5  ANN 6  SVM 3  SVM 4  
(1)  12  −26.88  −27.47  −20.84  −17.89  −29.45  −69.18 
(2)  26  49.00  14.41  14.84  37.66  6.31  −2.46 
(3)  20  11.24  −4.21  7.70  24.81  −4.03  −7.53 
(4)  35  −0.63  1.07  9.13  −45.02  11.06  28.42 
(5)  34  19.86  22.68  13.73  29.95  35.81  36.24 
(6)  17  34.71  26.11  −13.14  −12.66  −5.80  0.01 
(7)  17  −39.19  −27.34  −21.32  35.01  31.42  27.06 
(8)  20  −86.38  −38.59  −57.30  −93.00  −83.95  −41.62 
(9)  25  3.90  −14.67  −3.53  −2.19  5.42  −0.93 
(10)  35  −16.14  13.57  −0.55  6.17  −28.22  −1.72 
(11)  25  −61.40  −62.19  −55.17  −54.97  −17.28  −23.71 
(12)  38  0.66  −14.99  0.85  −1.74  21.63  19.26 
(13)  41  −124.67  −123.00  −113.63  −103.76  −66.12  −63.11 
(14)  60  −51.47  −53.54  −47.83  −56.72  −23.93  −24.82 
(15)  60  42.08  43.97  37.67  44.03  17.42  13.95 
(16)  60  −29.36  −24.88  −17.08  −30.75  −22.26  −19.72 
(17)  75  5.61  1.09  12.14  1.29  7.95  7.29 
MAPE 






Errors in the estimation of duration are higher when compared to estimation of offered prices for the realisation of works, since the investor defined in the tender documentation the maximum possible duration of works, which is more than an optimistic prediction. In other words, the offered time is not the result of the estimation by the contractor, but the limitation set by the investor. For this reason, contractors adopted automatically the maximum offered duration of works.
Additionally, it can be stated that models for separate estimation of costs and duration provide a higher level of accuracy than those which carry the estimation out simultaneously, which is specifically the case with ANN models. The reason for this lies in the fact that the impact of input parameters on the output ones is not the same for the estimation of costs and duration of works.
Figure
Analysis of sensitivity (ANN 3 and ANN 4); estimation of construction costs.
Figure
Analysis of sensitivity (ANN 5 and ANN 6); estimation of duration of construction.
Based on the graphs given above, it can clearly be noticed that the second group of input data has a considerably greater influence on the estimation of project duration, diminishing the influence of the first group. Moreover, the category of the project is of approximately the same significance for the estimation of both costs and duration as well, whereas the realisation zone has a considerably greater impact on the estimation of duration of the project.
Based on the results presented above, a conclusion was drawn that a greater accuracy level in estimating of costs and duration of construction is achieved by using of models for separate estimation of costs and duration. The reason for this lies primarily in the different influence of input parameters on the estimation of costs in comparison with the estimation of duration of the project. By integrating them into a single model a compromise in terms of the significance of input data is made, resulting in the lower precision of estimation when it comes to ANN models.
SVM models feature a greater capacity of generalisation, providing at the same time greater accuracy of estimation, for the estimation of both costs and duration of projects as well. In both cases, the greatest accuracy of estimation is achieved by the SVM model with 11 input parameters, that is, without a more considerable influence of the project realisation zone, which implies that the investors did not pay particular attention when defining the offered price and time to whether the works will be realised within the city zone or the suburbs.
Extending of the data base in terms of the very subject of a contract, that is, the future construction object, and the introduction of parameters, such as the length of the section, the roadway width, the urban road category (boulevard, side alley, etc.), the length of cycling lanes, areas intended for parking, pedestrian lanes, and plateaus, would widely extend the possibility of application of estimation models. There is a wide range of potential parameters which can be introduced as the feature of the construction object, presenting at the same time the input data for the estimation model. In this way, the possibility of estimating the costs and duration of construction in the contracting phase not only when the bill of works is defined (query by tender), but also when the investor defines only the guidelines, that is, the conditions that the future construction object has to meet (query by functional parameters of a future object), is created. Forming of a model with functional features of a future construction object as input parameters could have a double application from both perspectives, of the investor and the contractor. From the perspective of the investor, if the accuracy of estimation similar to that of already formed models was achieved, the precision in estimation in the initial phase and defining of criteria, which the future object is to meet, would be considerably above the one required by the literature (±50%) [
The research was conducted for the estimation of costs and duration of realisation for “build” contracts. This approach provides an option to estimate “designbuild” contracts as well; that is, the experience gained through “build” contracts can be used in future estimations of “designbuild” contacts, for both the needs of the investor and the contractor as well. Application of future models largely depends on the available information at the time of estimation. For this reason, the input data should be adjusted to the available information at the given moment, for both the investor and the contractor as well.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The work reported in this paper is a part of the investigation within the research project “Utilization of ByProducts and Recycled Waste Materials in Concrete Composites in The Scope of Sustainable Construction Development in Serbia: Investigation and Environmental Assessment of Possible Applications,” supported by the Ministry of Education, Science and Technological Development, Republic of Serbia (TR 36017). This support is gratefully acknowledged. The work reported in this paper is also a part of the investigation within the research project “Optimization of Architectural and Urban Planning and Design in Function of Sustainable Development in Serbia,” supported by the Ministry of Education, Science and Technological Development, Republic of Serbia (TR 36042). This support is gratefully acknowledged.