Construction Management and Economics (May 2005) 23, 423–430 Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques OBAID SAAD AL-SOBIEI1, DAVID ARDITI2* and GUL POLAT3 1 Technical Support Section, GDMW/MODA, PO Box 46539, Riyadh, 11542, Kingdom of Saudi Arabia Illinois Institute of Technology, Department of Civil and Architectural Engineering, Chicago, IL 60616, USA 3 Istanbul Technical University, Faculty of Civil Engineering, Istanbul, Turkey 2 Received 26 January 2003; accepted 2 December 2004 The construction project is subject to several risks, one of the most important of which is contractor default because contractor default may increase the final project cost considerably. In the US construction industry, owners commonly shield themselves from the risk of contractor default by transferring this risk to the contractor, who in turn transfers this risk to a surety company. On the other hand, the General Directorate of Military Works (GDMW) of the Kingdom of Saudi Arabia retains the risk of contractor default rather than transferring it to a third party. An artificial neural network (ANN) and a genetic algorithm (GA) are used in this study to predict the risk of contractor default in construction projects undertaken for the Saudi armed forces. Based on this prediction, the Saudi GDMW can make a decision to engage or not to engage the services of a contractor. In case the models are not able to generate reliable predictions (or generate contradictory outcomes), the GDMW will have to augment its budget with contingency funds to be used in the event of contractor default. The outcome of this study is of particular relevance to construction owners because it proposes an approach that can allow them to replace an indiscriminate blanket policy by a policy that is rational, effective, prudent and economical. Keywords: Artificial neural networks, contractor default, genetic algorithms, prediction model Introduction The construction process involves a large number of activities that are carried out by different parties. Each activity has its own risk, which results in accumulative associated risk for the project. These risks include the occurrence of unexpected events such as natural disasters, unforeseen site conditions, material and equipment delivery delays and equipment breakdowns. One such risk that may have very severe consequences on the owner’s plans is the risk of contractor default. The literature mostly claims that risk is improperly allocated in many construction projects (Kangari, 1995; Molenaar et al., 2000). This occurs because the owner tends to contractually pass responsibility for many project risks to the contractors. This is done via disclaimer clauses to fend off most risks and via *Author for correspondence. E-mail: [email protected] contract bonds to deal with the risk of contractor default. Contractors, in turn, must protect themselves by attaching premiums, hidden or explicitly identified, to their bid pricing. The contractor therefore shifts the risk back to the owner by charging an increased price for the work and by transferring the actual risk to an insurance and/or surety company. It is claimed that, usually, this indiscriminate shift of risk from the owner to the contractor really benefits no party. A more realistic and economical position is for owners to accept greater risk. Responsible owners ought to take the position that it is in their best interest to discriminate between project risks and to retain some of the risks while transferring others. The armed forces in the Kingdom of Saudi Arabia follow this premise. While they transfer some of the risks to contractors (who in turn buy insurance to cover themselves), they retain an important risk, the risk of contractor default. In the institutionalized practice in Construction Management and Economics ISSN 0144-6193 print/ISSN 1466-433X online # 2005 Taylor & Francis Group Ltd http://www.tandf.co.uk/journals DOI: 10.1080/01446190500041578 424 the contracts let out by the armed forces, contractors are not required to submit contract bonds at the time of the signing of the contract. If no (or very few) contractors ever default, i.e. if the risk of contractor default is nil (or extremely low), this approach appears to be reasonable. It would be wrong to assume, however, that no contractor ever defaults in Saudi Arabia. For example, in the 5-year study period, it was possible to identify 23 of a few hundred projects commissioned by the Saudi armed forces where contractors defaulted. It follows that the Saudi armed forces should have a clear understanding of the risk of contractor default and should allocate the necessary contingencies to their budget to meet the extra cost of probable contractor default. The objective of the study reported in this paper was to predict the likelihood of contractor default in projects commissioned by the General Directorate of Military Works (GDMW) in Saudi Arabia. The knowledge of the likelihood of contractor default will allow the GDMW in Saudi Arabia to contract only with desirable contractors (i.e. contractors that are not likely to default). It will also allow the GDMW in Saudi Arabia to allocate the necessary contingency to their budget in order to face the crises caused by defaulting contractors. Al-Sobiei et al. project, contract type and variation between the contractor’s bid price and the next lowest bidder’s price, etc.) (Russell, 1990, 1991, 1992; Kangari and Bakheet, 2001). The factors that affect the risk of contractor default are presented on the left side of Figure 1. It is postulated that the risk of contractor default is dependent upon the overall health of the contractor, the particular characteristics of the contract, and the nature of the project. The 23 factors listed in the three boxes on the left of Figure 1 include information that provides sufficient and indicative data to represent the decision pattern appropriately. The characteristics of the contractor, the particulars of the contract, and the nature of the project are defined in Tables 1, 2 and 3. respectively. The data were obtained from the files of the GDMW in Saudi Arabia. The function of this directorate is to supervise the construction projects on behalf of the Ministry of Defence in Saudi Arabia. The information retrieved was recorded in hard copy forms, and then input into the computer using Microsoft Excel. However, NeuroShell Predictor reads only text files (sometimes called ASCII files). Therefore, the file type was changed into a CSV (comma separated) format by using Excel’s capabilities. Contractor default prediction model Several studies have been carried out to understand the phenomenon of contractor default and to predict the failure of contractors by using discrete choice modeling (see Russell and Jaselskis, 1992; Severson et al., 1994) and stochastic modeling (Russell and Zhai, 1996). It is important to use the past history of similar situations to predict contractor default in construction projects. A prediction model that combines the abilities of artificial neural networks and genetic algorithms is proposed for this purpose. Once the likelihood of default is predicted, an action plan is recommended to the construction owner. The flowchart in Figure 1 shows the ‘Contractor Default Prediction Model’. Factors that affect contractor default The fundamental factors that are considered by surety bonding companies in the US during the contractor evaluation process are the contractor’s qualifications (i.e. financial strength, past experience, business plan, work capacity, quality and experience of the technical personnel, etc.), and project characteristics (i.e. work schedule, type, value, duration, complexity, location of Figure 1 Prediction model for contractor default Contractor default in Saudi Arabia Table 1 425 Contractor’s input attributes Contractor attributes Definition Type of business This attribute is described by one of three linguistic variables. Corporation, partnership, and individual proprietorship Contractors are classified into general, heavy/highway, residential, and others The dollar value is used as an input Type of contractor Value of largest project completed Value of largest uncompleted work Number of projects in progress Value of contracts in progress Number of past similar projects Construction equipment Number of years in construction business Number of workers Financial status The contract value of the job where the contractor defaulted is used as an input value. If this attribute is not applicable such as in the case of successful contractors a zero value is entered as input for this variable This attribute is an integer value. This attribute is related to the capability of the contractor of handling several jobs at the same time This dollar value represents all in-progress works. This variable is related to the capacity of the contractor This attribute describes how much a contractor is familiar with the type of project in question One of three linguistic terms (owned, leased, and rented) are selected to describe the status of this input attribute This attribute describes how much a contractor is familiar with the type of project in question The number of workers includes the entire job site and the office workers. This attribute is related to the capability of the contractor to execute the work The financial information was extracted from the bank report attached to the bid documents. This report shows the credit line of the contractor The data collected represent 21 projects, where the contractor defaulted and 33 projects where the contractor completed the project satisfactorily, of the projects commissioned by the GDMW in Saudi Arabia in a period of 5 years. Five additional random projects (in two of which the contractor defaulted and the remaining three were completed satisfactorily) were retained for testing. Prediction model NeuroShellH Predictor was the prediction software used in the study because it has two different training Table 2 strategies, namely the neural and genetic training strategies. The Contractor Default Prediction Model makes use of both training strategies. A brief description of these two strategies is presented below (NeuroShell Predictor, 2000). The neural training strategy solves problems whose solutions are difficult to define and do not follow linear patterns. Boussabaine (1996) conducted a review of artificial neural network (ANN) methods and their application in construction management. Risk analysis was pointed out as one of the fields in construction that could benefit from using ANN models. Hua (1996) compared ANN and multiple regression to determine Contract’s input attributes Contract attributes Definition Method of bidding This attribute is divided into three input variables namely (i) closed competitive bidding, (ii) open competitive bidding and (iii) negotiated bidding. Each one of the variables has a binary input (0 and 1). These variables are mutually exclusive This attribute has one of four linguistic values (i) traditional method, (ii) multiple prime contractors, (iii) force account method, (iv) design–build and (v) construction management. Each one of the variables has a binary input (0 and 1). These variables are mutually exclusive This attribute has one of four linguistic values (fixed price, cost plus, unit price, and lump sum). Since the rating of this attribute is discrete in nature, this attribute is divided into four input variables. Each one of the variables has a binary input (0 and 1). These variables are mutually exclusive This variable is expressed as a percentage and is calculated as follows: X5(next lowest bidder bid – contract price)/contract price Procurement method Type of contract Difference between contract price and the next lowest bidder 426 Table 3 Al-Sobiei et al. Project’s input attributes Project attributes Definition Project type Projects are classified into general, heavy/highway, residential, and other. Because it is not a continuous variable, this attribute is treated as four variables. Each one of the variables has a binary input (0 or 1) The contract price ($) was used as an input. This attribute shows how big the project is The value of this attribute is numeric in nature, so the number of months is used as input. This attribute is related to the size of the work This attribute was divided into two variables: head office in the same city as the project location, and head office not in the same city as the project location). Each one of the variables has a binary input (0 or 1) Project value Project duration Distance between project location and the contractor’s head office Project complexity Inflation Material availability Labor availability This attribute was obtained from the project supervisor and hence is subjective in nature. This attribute has one of three linguistic values (simple51, moderately complex52, and complex53) The inflation rates were extracted from the published statistical economic data for Saudi Arabia in the last 5 years This attribute is related to the availability of construction materials in the vicinity of the project’s geographic location. This attribute is expressed by two linguistic variables (materials available locally) and (materials imported). Each one of the variables has a binary input (0 or 1) This attribute deals with the issue related to the availability of construction labor in the vicinity of the project’s geographic location. This attribute is measured by project supervisors’ experience regarding the labor environment at the time their project was underway. These linguistic values include (limited labor availability51, low labor availability52, high labor availability53) whether ANN can produce better predictions than multiple regression. The forecasting error of Hua’s (1996) ANN model was found to be about one-fifth of that derived from multiple regression models. The ANN begins by finding linear relationships between the inputs and the output. Weight values are assigned to the links between the input and output neurons. After those relationships are found, neurons are added to the hidden layer so that non-linear relationships can be found. Input values in the first layer are multiplied by the weights and passed to the second (hidden) layer. Neurons in the hidden layer ‘fire’ or produce outputs that are based upon the sum of weighted values passed to them. The hidden layer passes values to the output layer in the same fashion, and the output layer produces the desired results (predictions). The network ‘learns’ by adjusting the interconnection weights between layers. The answers the network produces are repeatedly compared with the actual answers, and each time the connecting weights are adjusted slightly in the direction of the correct answers. Additional hidden neurons are added as necessary to capture features in the data set. The genetic training strategy trains more slowly than the neural strategy (NeuroShell Predictor, 2000). The genetic training strategy obtains better results if the data being tested are similar to the training data (NeuroShell Predictor, 2000). It also works better when the training data are sparse. A genetic algorithm (GA) solves optimization problems by creating a population or group of possible solutions to the problem. The individuals in this population will carry chromosomes, which are the values of variables of the problem. The GA actually solves the problem by allowing the less fit individuals in the population to die and selectively breeding the fittest individuals (the ones that solve the problem best). This process is called selection, as in selection of the fittest. The GA will take two fit individuals and mate them (a process called crossover). The offspring of the mated pair will receive some of the characteristics of the mother, and some of the father. In nature offspring often have some slight abnormalities, called mutations. Usually these mutations are disabling and inhibit the ability of the children to survive, but occasionally they improve the fitness of the individual. The GA similarly occasionally causes mutations in its populations by randomly changing the value of a variable. After the GA mates fit individuals and mutates some, the population undergoes a generation change. The population will then consist of offspring plus a few of the older individuals that the GA allows to survive to the next generation because they are the most fit in the population, and one will want to keep them breeding. These most fit individuals are called elite individuals. After dozens or even hundreds of ‘generations’, a population eventually emerges wherein the individuals will solve the problem very well. In fact, the fittest (elite) individual will be an optimum or close to optimum solution. The processes of selection, crossover and mutation are called genetic operators. 427 Contractor default in Saudi Arabia The 23 factors described in Tables 1 (contractor characteristics), 2 (particulars of the contract) and 3 (nature of the project) were used as input variables. The output variable is the likelihood of contractor default. Action plan The costs associated with contractor default may include: (1) loss of profits due to the project not being completed on schedule; (2) administrative expenses to analyze the situation, assess the amount and quality of work completed, and determine the next course of action; (3) cost to complete the project beyond the money yet unpaid to the contractor; (4) fees associated with the resolution of conflicts involving the contractor and the owner; and (5) negative publicity and loss of goodwill within the community. A contingency allowance to avoid project overruns arising from unexpected events can be calculated in various ways such as by using a probabilistic model (Touran, 2003) and risk analysis (Mak and Picken, 2000), the most common way in the US being to consider around 10% of the estimated cost (Burger, 2003). An owner should know how to assess risks and to take action in order to minimize costs, avoid delays, and eliminate claims and disputes. Risk management techniques include risk avoidance, risk financing or risk control. Risk avoidance presumes the knowledge of a reasonably accurate prediction of the outcome of the risk event; a party may try to avoid an activity altogether based on the knowledge that a risk event will probably disturb that activity. For example, an owner may decide not to be involved with contractors whose likelihood for default is high, assuming that such predictive knowledge is available to the owner. Risk financing refers to methods of funding losses. The primary sources of risk financing are: N N Risk transfer: the contractor most often transfers the risk of contractor default to a surety company through the submittal of contract bonds. Performance and payment bonds ensure that the contractor will complete the project in accordance with plans and specifications and will pay all the parties that provide services and/ or materials. All public construction in the US with very few and minor exceptions have to be bonded according to the Miller Act. Transferring risk does not reduce the criticality of the source of the risk; it simply transfers it to another party, i.e. the surety company. Risk retaining: not all risk can be transferred, but even if a risk could be transferred it may not prove to be economical to do so. The risk will then have to be retained. The Saudi Armed Forces appear to have chosen this route when dealing with the risk of contractor default, as no guarantees are required from contractors at the time of the signing of the contract. Risk control involves taking measures to minimize the possibility of the risk occurring and/or to minimize the effects of the risk event in case such an event actually occurs. Risk control can be achieved by: N N Risk prevention: in the context of this research, risk prevention means taking steps to prevent a contractor from defaulting. This may take the form of temporary financial or logistics support to allow a contractor to overcome a bottleneck. Risk reduction: reduction involves minimizing the consequences of contractor default once it has occurred. For example, speedy contracting procedures and the use of cost + contracts may allow an owner to replace a defaulting contractor in minimum time and at minimum extra cost. Findings and discussion When NeuroShell Predictor was run using the neural network training strategy, experiments had to be conducted to determine the optimum number of hidden neurons for obtaining the best results. The correlation (r) between the actual and predicted outputs was calculated against an increasing number of hidden neurons as they are added to the network and graphed in Figure 2. This graph performs a statistical measure of the ‘goodness of fit’ between the actual and predicted outputs and shows that all correlations are very close to 1 when using up to 14 hidden neurons. Similarly, the mean squared error (i.e. a statistical measure of the differences between the actual values and the predicted values) was also calculated and plotted against the number of hidden neurons as they are added to the model (Figure 3). In this measure, the errors are squared to penalize the larger errors and to cancel the effect of the positive and negative values of the differences. Based on the mean squared error criterion, it was apparent in Figure 3 that the best results were again obtained with 14 hidden neurons in one hidden layer. Finally, the performance of the network was also assessed by measuring R-squared, also known as the coefficient of multiple determination. R-squared is a statistical indicator that compares the accuracy of the model to the accuracy of a trivial benchmark model wherein the prediction is just the average of all of the example output values. A perfect fit would result in an R-squared value of 1, a very good fit 428 Al-Sobiei et al. Table 4 Figure 2 neurons Correlation coefficients vs. number of hidden Figure 3 neurons Mean squared error vs. number of hidden near 1, and a poor fit near 0. If the neural model predictions are worse than one could predict by just using the average of the output values in the training data, the R-squared value will be 0. The maximum Rsquared value was reached when 14 hidden neurons were used in one hidden layer. When NeuroShell Predictor was run using the genetic training strategy, the number of generations, Table 5 Project # 1 2 3 4 5 Training statistics Training strategy Rsquared Average error Correlation coefficient r Mean squared error Neural Genetic 0.98 0.96 0.05 2.09 0.99 0.98 0.01 62.33 and the parameters that need to be optimized (RSquared, average absolute error, correlation coefficient, mean squared error) had to be specified. The number of generations allowed by Neuroshell Predictor ranges between 10 and 1000. The default value of 100 generations was used in the study. The program was also directed to maximize R-squared and the coefficient of correlation and to minimize the average error and the mean squared error. The statistical reports generated by NeuroShell Predictor are presented in Table 4. The values related to the neural network training strategy were obtained by using 14 hidden neurons. All statistical indicators (i.e. the correlation coefficient r, the R-squared value, the average error and the mean squared error) show that training has been conducted successfully when using both the neural network and genetic training strategies. The correlation coefficients are very high (the lowest being 0.98) and errors are quite low. The statistical indicators appear to be comparable for the neural network and the genetic training strategies. To find the effect of the order in which the input data is entered into the system, fifty different orders were selected at random. All these trials gave similar results, which mean that the outcome of the training does not change when the order of the input data is changed. Once the neural network and genetic training strategies were used to predict contractor default, the model was then tested by means of data collected from five projects that had been selected at random and put aside for this purpose. In two of the five projects the contractor had defaulted whereas the remaining three projects were completed satisfactorily. According to the results presented in Table 5, the two training strategies display comparable performance by predicting correctly the outcome of three of five Prediction of contractor default Actual occurrence of default Default (1) Default (1) No default (0) No default (0) No default (0) Predicted occurrence of default using neural strategy Predicted occurrence of default using genetic strategy Default (1) Default (1) N/A N/A No default (0) Default (1) N/A N/A No default (0) No default (0) 429 Contractor default in Saudi Arabia projects. In this evaluation, any result within the range ¡0.5 is considered to have a value of 0 (no default), while any result beyond the range ¡0.5 is considered to have a value of 1 (default). ANN and GA were not able to predict the outcome of two of the five projects. The genetic method has built-in protection against making predictions for which it has no basis to make them. That is, it will not try to make a prediction for a pattern if the training set did not include other patterns similar to that pattern. The pattern will be marked as N/A, meaning ‘unpredictable’ (NeuroShell Predictor, 2000). Similarly, given the information in the training set, ANN may not be able to make a prediction, in which case we marked the results as N/A. N/A simply means that the system is not able to make a prediction, i.e. the result is inconclusive. The two training strategies produced the following results in the context of the five projects tested in this study: N N N The predictions generated by the two strategies concerning Projects 1 and 5 agree with each other and with the actual occurrence. There is of course no guarantee that the two training strategies will always produce consistent predictions. It was not possible to predict the outcome in Project 3 using either strategy. While the outcome in Project 2 could not be predicted using the genetic training strategy it was correctly predicted by the neural network strategy, but while the outcome of Project 4 was correctly predicted by the genetic training strategy, it could not be predicted using the neural network training strategy. The last stage in the risk management process is to evaluate the alternative decisions in the light of the output of risk analysis. In the context of the risk of contractor default, this process starts with deciding whether the owner prefers to avoid, transfer or retain the risk. Given the established practices in the GDMW in Saudi Arabia that does not require the use of contract bonds, it is out of the question for the GDMW to transfer the risk to the contractor and, by implication, to a surety company. The only option this agency has is to avoid or retain the risk of contractor default. If both of the training strategies indicate that the contractor is likely to default (or if one strategy predicts default while the other strategy is inconclusive), then it is prudent for the owner to avoid contracting with this company. On the other hand, if both training strategies concur that the contractor will not default (or if one strategy predicts no default while the other is inconclusive), then the owner should be able to sign a contract with the said contractor assuming that the risk of contractor default is quite low. However, some minimum contingency should be allocated to cover for the inaccuracies in the predictions. The information collected from the 23 contractors who defaulted indicates that the losses associated with contractor default range between 9% and 24% of the contract value, with an average of 15% weighted by contract size. It should therefore be possible for the GDMW in Saudi Arabia to budget a minimum contingency of 9% to deal with losses in case of contractor default even if the ANN and GA methodologies predict otherwise. If neither training strategy is able to generate a prediction, or if the predictions generated by the two strategies are contradictory, the next step involves risk control, i.e. the prevention and reduction of the consequences of probable contractor default. The GDMW in Saudi Arabia should therefore budget an appropriate contingency and to be prepared to support the contractor with financial and logistic resources in case the contractor defaults. If the General Directorate’s decision-making process is governed by a ‘risk-seeking’ behavior, it is recommended that it budgets 9–15% of the contract value as a contingency against probable contractor default. For a ‘risk averse’ posture, the budgeted contingency should be 15–24% of the contract value whereas for a ‘risk neutral’ posture, the contingency may be taken as around 15% of the contract value. Conclusion and recommendations Contractor default is one of the most costly risk events for an owner. Construction owners in the US typically transfer this risk to the contractor who in turn transfers it to a surety company. In the US practice, the contractor has to submit contract bonds to the public owner (and often to the private owner) at the time the contract is signed. However, according to the established practice in the projects let out by the GDMW in Saudi Arabia, contractors are not expected to submit contract bonds. In other words, the GDMW always retains the risk of contractor default; but Saudi contractors do default at times and the GDMW has to deal with the consequences, i.e. the losses and delays associated with such defaults. It is claimed in this study that automatically retaining the risk of contractor default is not the only option available to the GDMW, and that the option of avoiding the risk altogether is also available if the likelihood of contractor default can be predicted prior to award. Furthermore, the GDMW could be prepared for the risk of contractor default in case such prediction is not possible. ANN and GA were used in this study to predict the likelihood of contractor default. The analysis makes use 430 of raw data typically collected by the GDMW in its projects undertaken in Saudi Arabia. The ANN and GA models were tested using independent test cases to demonstrate their validity. If there is agreement between the training strategies concerning the prediction of the outcome, the GDMW can make the decision of hiring (but allocating minimum contingency funds) or not hiring the said contractor. If, on the other hand, the predictions do not match or if no prediction is possible using either strategy, then the GDMW may hire the said contractor but must allow for some contingency fund in its budget to accommodate the probable losses to be incurred in case of contractor default. The amount of the contingency would be dependent on the risk policy (riskseeking, risk neutral, or risk averse) of the GDMW at the time the contract is awarded. The study is limited by the small number of training and testing cases. No generalizations are possible, given the small number of cases compared to the large number of projects handled by the GDMW over the years. However, the study shows that a strategy exists that could allow big construction owners to minimize the overall cost of contractor default. Established practices in some countries require that all contractors be automatically bonded regardless of risk of contractor default (e.g. public projects in the US). Established practices in some other countries require that no contractor be bonded regardless of risk of contractor default (e.g. armed forces projects in Saudi Arabia). The premise of this study is that such blanket policies do not discriminate between the various alternatives available to owners and are not conducive to economical solutions. The alternatives to bonding all contractors regardless of the risk of contractor default are investigated elsewhere (Al-Sobiei et al., 2004). The alternatives to not bonding any contractor are investigated in the study presented in this paper. The outcome of this study is of particular relevance to construction owners because it proposes an approach that can allow them to follow a policy that is rational, effective, prudent, and economical. 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