1. Introduction
An important condition accompanying smooth and consensual implementation of a construction project is a proportional and transparent division of risks between the parties of the contract, inter alia, the consequences of any disruptions arisen from an increase in the scope of work and extension of the completion time [
1]. In this context, properly structured legal and contractual solutions are crucial, as they significantly reduce the risk of conflicts between cooperating parties and, in many cases, offer the chance to solve the aforementioned problems without any court involvement [
2,
3].
During the construction works execution, various types of unforeseen circumstances occur, affecting the course and progress of works causing the contractor’s financial loss. In practice, the factors causing serious disturbances in the contractor’s operations include the necessity to introduce changes and revisions in the scope of works (so-called change orders, e.g., due to design faults), lack of access to the construction site at the planned date, the necessity to suspend works and re-mobilize, logistic problems related to supplies, organization and coordination of works conducted by several subcontractors, adverse weather conditions [
4]. The numerous disruptions are particularly severe for contractors in the current context of the ongoing COVID-19 pandemic, and resulted, e.g., in delays in the delivery of materials and equipment, slowdowns in operation due to the need to comply with existing travel restrictions, and most importantly, increased and unpredictable worker absence [
5]. The occurrence of events causing a construction project cost increase and extension of its duration means that to achieve the effect agreed between the parties at the stage of signing the contract, the contractor must incur additional expenditures—higher than originally assumed. In practice, obtaining financial compensation from the ordering party (a client) is often a source of a dispute between the parties to the contract, which in many cases, may be resolved only in a court [
6,
7,
8]. One can speculate that the negative effects of a continuing pandemic, in the long run, will lead to numerous conflicts between construction investment parties and increased litigation.
The results of a survey [
9] conducted by Contract Advisory Services (CAS) among entities related to the construction industry in Poland (representatives of investors and contractors, dealing with the implementation of a construction project management, handling disputes between the parties to the investment process, valuation of works, including, inter alia, lawyers, engineers, management, technical, financial and administrative staff) indicate that the average value of a dispute in which the respondents participated in 2019 was PLN 52.7 million (in 2018—PLN 52.6 million). According to the forecast of experts, it will increase in the following years [
9]. Disputes involving respondents in 2018 lasted an average of 29.2 months [
9]. The results of the report [
9] also indicate that in 2019, the construction industry in Poland saw an escalation of conflicts related to the implementation of previously concluded contracts, an increase in the number of ineffective tender proceedings and court proceedings.
According to [
9], the practice-relevant causes of disputes between contracting parties are:
increase in costs of contract execution (according to 85% of respondents),
missing or delayed key decisions (according to 63% of respondents),
different conditions at the construction site compared to those specified by the ordering party (a client) (according to 51% of respondents),
deficiencies and faults in documentation for investments conducted in the "design and build" formula (44% of respondents) and " build" formula (29% of responses),
incorrect contract administration (22%), lack of understanding of the contract by the parties and failure to meet their contractual obligations (according to 20% of respondents),
missing or delayed payments (20%),
disruptions caused by adverse weather conditions (17%).
According to the respondents [
9], disputes arising at the stage of implementation of the contract matter do not find an amicable settlement due to:
fear of contractual parties of being responsible for the decisions made (according to 86% of respondents),
divergent perception of the purpose of the contract as a conflict of interests between the parties (according to 44% of respondents),
unwillingness to take action (34%),
ignorance and lack of qualifications of cooperating entities (19%).
Among the most popular tools for resolving disputes, respondents [
9] indicated primarily the common court (71%) and the "wait-and-see" method (68%), but also negotiation (39%), mediation (3.5%) and arbitration (3.5% of respondents). Respondents considered negotiation (78%), common court (35.5%), mediation (29%), arbitration (15%) and conciliation (13.5%) to be the most effective methods of dispute resolution. The "wait-and-see" method was not considered an effective tool for resolving disputes between contracting parties (5%). It should be noted that the answers of the respondents show a clear disproportion between the methods that in practice are most often used to resolve disputes and those that are considered to be the most effective.
It may be assumed that in practice a combined strategy for resolving disputes is used. Initially, the conflicting parties try to wait out the situation (being fully aware of the ineffectiveness of this method), and in the next stage, they transfer the responsibility for resolving the dispute to a common court. This strategy is closely related to the fundamental causes of disputes between cooperating parties, which include lack of decisiveness, the inertia to make decisions, fear of liability, and passivity to take action. An additional factor pointed out by contractors is the significant increase in the costs of construction projects and the lack of adequate valorization formulas in the contents of contracts to reflect the actual level of changes in construction output prices [
10,
11]. As a consequence, they result in unprofitable contracts, ineffective solutions and high social costs. Completion of an uncompleted construction contract by a contractor selected in a new tender procedure is more expensive than, for example, increasing the amount of the original contractor’s remuneration or adjusting the amount of remuneration stipulated in the contract, which currently cannot be performed by the contractor due to a drastic price increase. In such circumstances, it is reasonable for the contractor to seek an independent judicial resolution of the dispute [
12,
13].
The results of a survey [
9] conducted by CAS indicate that projects of large scope and long duration, implemented with public funds (under the provisions of the Public Procurement Law) and by large entities (e.g., government agencies) are primarily exposed to serious disputes between contracting parties. Public sector investments (primarily road, rail and energy infrastructure construction) mainly due to the high uncertainty of the contractor regarding the terms of performance of the contract matter, are considered to generate more disputes than private projects [
9,
14]. The scale of these investments makes the cost increase of their implementation significant. According to [
9], the largest number of disputes occurs during the implementation of road infrastructure (in 90% of cases) and rail infrastructure (47% of cases). Public procurers are considered difficult business partners, characterized by a high aversion to amicable solutions. This is caused by, among other things, systemic solutions, the obligation to apply public finance discipline, and legal regulations which significantly limit flexibility, e.g., in disposing of funds and making independent decisions that consider the current circumstances of investment implementation. It may be assumed that a large number of infrastructure projects and, at the same time, the reluctance of contracting authorities to find out-of-court solutions to disputed situations will result in an increase in the number of court proceedings in the coming years.
To sum up—the practice shows that common courts and legislation fail to keep up with the frequent changes that occur in the construction process, in the area of technology, construction organization, financial and insurance instruments. These new solutions of different nature undoubtedly influence the length of proceedings, their complexity and costs connected to dispute settlement. Regardless of its original cause, a dispute where the parties involved in the project cannot find an agreement and a way to resolve the conflict within the mechanisms provided in the content of the concluded contract, is usually settled in a court. Such a solution is not beneficial for any of the parties involved—it requires a long time to wait for a court decision and generates additional costs. In this context, alternative dispute resolution tools should be taken into account, that allow to find a quicker and a relatively cheaper method to solve a conflict such as negotiations, mediation and arbitration. Moreover, in public procurement contracts, a clear asymmetry in the distribution of risks between the parties to the contract occurs. In the current situation of instability in the construction market, any changes in the project environment particularly affect in particular one of the parties to the contract. Additionally, the disproportionate distribution of the parties’ responsibilities and rights in the contract give rise to difficult relationships, conflicts and, ultimately, disputes settable only in a court. In practice, the interests of the contracting authority are better protected than those of the contractor. This is caused mainly by the fact that the terms of contracts are prepared by the contracting authority, which include requirements arising under the Public Procurement Law, and they are not subject to negotiation, so contractors do not have the opportunity to introduce clauses that protect their interests. This results in a long-term litigation and the dominant position of the ordering party. Its favorable contractual provisions cause, that in many cases, the bad financial situation of the contractor is further aggravated in a court. For this reason, a contractor’s decision about legal action is fraught with additional risk and multicriteria estimation of potential gains and losses [
15,
16].
Decisions can be supported with multicriteria methods [
17,
18,
19], but also with machine learning tools—one of possible approaches supporting this process is Bayesian statistical decision theory providing a mathematical model to make decisions in conditions of uncertainty [
20]. In the context of disputes in construction industry, the authors decided however to use decision trees (DT) and artificial neural networks (ANN) considering their application values.
Machine learning tools are widely used to support decision problems. The existing models predict the occurrence of construction disputes and provide decision-support information necessary to select the appropriate resolution strategy before a dispute occurs [
21,
22]. Other studies focus on investigating factors affecting the outcome of litigation, as well as on predicting the outcome of construction litigation itself [
16,
23,
24,
25]. In order to predict the optimal solution in a conflict situation, the authors applied various tools, including ANN [
16,
22,
25] and DT [
16,
22,
23], having based on data from a wide variety of sources: directly from courts, online databases, literature. The data was frequently collected from a wide variety of construction projects executed in many different countries and obtained from many different construction companies. Therefore, the novelty of the proposed method of a decision support is based on the historical dispute cases of only one contractor. What is more, predictions are based solely on time and financial data usually collected by a contractor.
The subject of the article is quantitative risk assessment in construction disputes based on machine learning tools. The article presents the most common causes of conflicts between parties of the construction contract, defines the background of the problem as well as introduces an example incorporating a real-life problem. By using DT and ANN the authors present application possibilities of the tools supporting the contractor’s decision-making process in the conflict situation with a client.
The process of getting to the proposed decision support method is presented in
Figure 1.
The applied tools, i.e., artificial neural networks (ANN), decision trees (DT), and association analysis are presented in
Section 2. Then, the association rules concerning the provided real dataset on construction contracts problems are found. They are the base of a much wider database, simulated and described in
Section 2.2. The full simulated database is presented in
Appendix A. Then, in
Section 3, the accuracy of automatic classifiers is verified on that extended database. To model other, less structured cases the database is step by step modified, distorted and the accuracy of the classifiers is checked at every level of modifications. The results achieved in
Section 3 are discussed in
Section 4. There is also an example of application the proposed working-out the decision together with the proposed the risk read-out from the machine learning models that support the decision-making process. The findings are summarized and concluded in
Section 5.
4. Discussion
To interpret the results obtained by the tools applied, it is necessary to analyze the confusion matrix and the corresponding indicators to assess the diagnostic value of the classification.
In the field of machine learning and specifically in the problem of statistical classification, a confusion matrix is a table layout allowing visualization of the performance of an algorithm [
68]. In this case, each row of the matrix represents the examples in an actual (observed) class, while each column represents the examples in the predicted class. The four possible outcomes of the matrix are:
True Positive (TP),
True Negative (TN),
False Positive (FP; type I error or underestimation),
False Negative (FN; type II error or overestimation).
Considering the problem analyzed, which is the conflict between the contractor and the client, the confusion matrix presented in
Table 17 indicates the possible variants of strategy from the contractor’s perspective.
The effectiveness of the classification performed through DTs and ANNs was evaluated in terms of accuracy, recall and specificity (Formulas (25)–(27)) [
69].
Accuracy (ACC) indicates the proportion of correct classifications, however, it may yield misleading results if the data set is unbalanced [
68]. Hence, as a complement, it is advisable to analyze recall, which is a true positive rate (probability of detection), as well as specificity, which is a true negative rate. All the results presented in
Section 3 are transformed to the following ratios: ACC, recall and specificity. They are presented in
Table 18.
For every type of dataset, the DT tool outperforms the ANN classifier when accuracy and recall are considered. The specificity is better for the ANN classifier, for each dataset. The results presented separately for ANN and DT (see
Figure 7 and
Figure 8).
The level of distortion of the data from the simulated dataset (mod-0) based on the rules found in the original dataset increases from mod 1 to mod-3. It influences a lot the recall of ANN. It decreases rapidly, while the decrease of the specificity is not considerable. It is due to the distortions made to the cases with s = 1 (clients sued). The subset of 69 cases with s = 0 is not modified for the purpose of creating mod-1 to mod-3. This made the specificity of ANN high. The recall of decision trees presents much higher resistance for the distortions made to the datasets. Their classifications (when w = 1 is presented as the result) are not perfect (as for mod-0). Nevertheless, the levels of recall are high, every time above 93.5% for mod-1 and mod-3 (and 100% for mod-0). Recall and specificity for DT and ANN are compared in
Figure 9.
We analyze the risk of failure i.e., wrong decision based on ANN classification, assuming that the mod-1 database reflects the real case. If the classifier predicts w = 1, it is suggested to make the decision of taking a client to a court. Its recall (presented in
Table 18) is 75.8%. The false-positive rate (FPR) [
68,
70] is then:
and it is equal 24.2%. Then, there is a danger of losing the case in a court. Therefore, it is a risk of wrong decision undertaken on the basis of the supporting model (w = 1 is predicted). Similarly, when w = 0 is suggested by the decision supporting tool, the risk of wrong decision (based on ANN classification result) is equal to false-negative rate (FNR) defined as:
As the specificity of ANN for mod-1 is 95.0%, the risk of making the wrong decision is 5.0%. In case of materializing such risk (and not taking a client to a court), GC would lose benefits from a potential win in a court.
A similar reasoning can be made with a DT use, but it is recommended to utilize the feature of DT for a clear presentation of the process of classification in a form of a tree. Based on the same assumption (mod-1 represents a real case), the parameters of an analyzed project should be matched with the set of conditions of the tree (presented in
Figure 6) until a leaf is reached. If the reached leaf suggests w = 1, e.g., it is ID = 12 leaf, the risk of a wrong decision is 50%, but for leaf ID = 12 the risk is 6/19 = 31.6%. However, it would be a risk-free decision if based on ID = 9 leaf.
It is recommended that the decision of taking a client to a court is supported by both models (ANN and DT). In case of agreement between suggested decisions from both models, the suggestions may be considered. The opposite suggestions found with these two models require comparison of the risk of each suggestion (calculated in different ways for ANN and DT), as well as, taking into account the policy of a specific GC. In case of w = 0 falsely suggested, GC will lose potential benefits. In case of falsely suggested w = 1, additional, not covered cost will be engaged without any benefit.
Analyzing the problem X (presented in
Table 1 and described in
Section 2.2.1.) it can be found that (considering the original 10-row dataset) the project X data meet at least two rules found. The project X value
is greater than 18.60, so based on the rule found and presented in (8) there is 100% confidence of a favorable sentence in a court. Additionally, the rule presented in (12) is met. The total additional costs of the project X are not covered by the financial reserve. This makes the confidence 100% of winning the case in a court. However, there is the rule (13) which is not met by the parameters of the project X i.e., the delay of the completion date is lower than 40% of the scheduled time. Therefore, this rule indicating the loss in a court (if met), is not met. It is one more argument to sue a client.
Let us then assume that the project X was executed by a company, for which the mod-0 dataset is valid. If DT (presented in
Figure A1) is used, the following way to reach a leaf should be taken, starting from split node ID = 1:
, the node ID = 2 should be considered then;
, the node ID = 5 should be considered then;
, the node ID = 8 should be considered then;
. The leaf ID = 11 is reached. The read-out from the leaf is as follows: there are 14 projects meeting the same criteria (stated in split nodes), and for all of those disputes there were favorable court sentences. DT suggests the risk-free decision of taking a client to a court in case of project X. ANN classification confirms w = 1 for the project X input with recall 75.8%, so there is a 24.2% risk of a wrong decision (if the suggestion is considered).