Automation in Construction 17 (2008) 773–779

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Automation in Construction

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KNN based knowledge-sharing model for severe change orderdisputes in construction

Jieh-Haur Chen ⁎Institute of Construction Engineering and Management, National Central University, 300 Jhong-da Rd., Jhongli, Taoyuan 32001, Taiwan

a r t i c l e i n f o

⁎ Tel.: +886 3 4227151x34112; fax: +886 3 4257092.E-mail address: jhchen@ncu.edu.tw.

0926-5805/$ – see front matter © 2008 Elsevier B.V. Aldoi:10.1016/j.autcon.2008.02.005

a b s t r a c t

Article history:Accepted 13 February 2008

Changes during a construction project are inevitable but many projects are also plagued by severeconstruction disputes triggered by such changes. These disputes can become time consuming and costlyproblems which may require litigation to resolve. The objective of this research is to develop a knowledge-sharing model for information sharing that will effectively aid the interested parties to avoid litigiousconstruction change disputes. This model is developed by first establishing a comprehensive database,followed by K Nearest Neighbor (KNN) pattern classification. The data used for the modeling is collected froma nationwide investigation of U.S.A. court records. The model is designed to provide knowledge-sharinglinking to the behaviors of similar construction participants with the goal of facing possible serious disputescaused by changes in construction orders. The benefits of this research are not only the development ofknowledge modeling but also to help construction practitioners utilize knowledge sharing to preventunnecessary expense and loss.

© 2008 Elsevier B.V. All rights reserved.

Keywords:Construction managementKnowledge sharingLitigationDisputesChange ordersKNN

1. Introduction

Changes often need to be made during a construction project andare necessary to optimize the benefits. They happen frequently in theconstruction industry. Nevertheless, these changes can lead todisputes among the construction project participants, often seriousenough to require litigation to solve. Unfortunately court proceedingsare usually a negative and costly experience for all parties involved.The characteristics and consequences of construction project changesand disputes arising from them in the construction industries arediscussed in a number of contract documents and research papers.Construction practitioners have gradually realized that projectchanges are reasonable; even so, cases of litigation due to projectchanges still occur. Although studies demonstrate that informationtechnology (IT) has facilitated knowledge sharing in a wide varietyof ways such as the use of electronic databases and mathematicalmodels [1,2], a survey conducted in 2005 found that sharing of knowl-edge in the construction industry was still not common [3]. That studyconcluded that there are 12 benefits that contracting companieswouldgain if they adopted knowledge sharing [4]. One would think thatknowledge sharing would thus appear extraordinarily attractive in thehighly competitive, experience-oriented environment of the construc-tion industry. We summarize and outline the problem to be dealt within this study as follows: (1) Severe construction disputes are oftentriggered by changes in orders andmay require litigation, which brings

l rights reserved.

heavy costs to both parties and should be avoided by knowledgesharing if at all possible; and (2) an alternative resolution provided byknowledge sharing is desirable. To solve these problems we develop amathematicalmodel based on the knowledge-sharing concept to avoidthe possibility that a given change ordermight lead to future litigation.

2. Knowledge management and knowledge sharingin construction

Since first being introduced in 1995, Knowledge Management(KM) has received substantial attention by both the constructionindustry and academia [5]. A survey conducted by a project-basedorganization in the United Kingdom found that approximately 50% ofrespondents from the majority of the construction industry felt thatKM could be of benefit to their organization and would bring aboutthe use of new technologies and processes. The significant needs forKM in the construction industry were identified as: need forimprovement in the sharing of valuable knowledge, the disseminationof superior practices, customer response, reduction of reworking, anddevelopment of new products and services [6]. Of these, knowledgesharing plays a key role, and has been often discussed in relation to theconstruction industry. For example, in one study the idea thatknowledge is conceptualized by construction project managers ischallenged and knowledge sharing is suggested as a more feasiblealternative [7]. In another study the transfer of project knowledge orpartnering to share knowledge is discussed [8]. The focus of KMresearch has been extended to all facets of the construction life cycle[9]. Knowledge in the construction domain may be divided into threecategories, domain knowledge, organizational knowledge, and project

Table 1Criteria for investigation of litigation archives

Project data

What is the project type? Residential, commercial, industrial,institutional, infrastructure, military,waste disposal or maintenance

What type of construction is being donewith this project?

Addition/expansion, new, renovation

What is the project size in dollars? $_____Is there a private or public owner privateor public?

Private or public

Who are the plaintiff and the defendantfor this litigation?

General vs. subcontractor, CM vs. generalcontractor, or owner vs. general contractor

What is the contract type for this project? Lump sum, cost plus fee (% or fixed,guaranteed max), unit price, verbalestimate, etc.

What is the estimated project duration atcontract award?

_____ calendar weeks

What is the actual project duration aftercompletion if finished?

_____ calendar weeks

Is there a fixed end date? Yes or noHas a schedule extension been asked for? Yes or noHas a schedule extension been granted?Why is it needed?

Yes or no

Disputed issue data

What are the reasons raised for thedispute?

Finance, performance, payments, contract,changes, etc.

What is the main type of dispute in theproject?

Cost, schedule, both

Have changes occurred in the project? Yes or noWas there a written contract request forchange orders

Yes or no

Was there any arbitration before thelitigation?

Yes or no

Did the disputes result in partial or totalwork stoppage?

Yes or no

Number of owner initiated changes: _____Number of contractor initiated changes: _____Number of changes submitted: _____Number of changes approved: _____How many changes caused disputes? _____Additional compensation approved: $_____Additional compensation argued: $_____Possible reasons for changes, indicating %of actual executed man-hours of changeorders

Additions, change in code, change in tech.,deletions, design change, etc.

774 J.-H. Chen / Automation in Construction 17 (2008) 773–779

knowledge [10]. Advances in computer technology, networking, andcommunications have helped to promote knowledge sharing. Manyresearchers advocate that the integration of computer technologiescan improve knowledge sharing primarily through facilitating therelationships between component interfaces. This integration wouldbenefit knowledgemanagementwithin companies [11]. A knowledge-sharing workbench has proven to be one effective and efficientstrategy for engineering design integration as engineering designshave become more and more complex [12]. Other researchers haveexplored the benefits that knowledge sharing can bring to the con-tractual companies [4]. Theuse of IT to facilitate knowledge sharing hasbeen amajor force to improve performance [13]. Knowledge sharing isalso fundamental for learning lessons in construction disputes.

3. Change-order-triggered disputes in the construction industry

Disputes between owners, project managers, and contractors arecommon occurrences during the construction process. Projectchanges frequently go hand in hand with disputes, regardless of thetype of change. The Dispute Review Board (DRB) has been introducedto solve such disputes. The Metro Tunnel case study has concludedthat disputes can be divided into three categories [14,15]. The disputestriggered by project changes often arise when construction practi-tioners are forced to change orders to solve some dilemma, but thesechanges are acceptable to all the participants in the project [16–19].Litigation is the most expensive and time consuming last resortsolution. No one intends to lead his or her project into a lawsuit at thebeginning of the dispute. Litigation can have a momentous effect onthe plaintiff, the defendant, and even the entire project. Disputes arisefrom different elements which vary with the nature and character-istics of the project. One of the most important causes for severedisputes is project change triggered by a variety of reasons thatare involved, such as owner-acknowledged changes, constructionchanges, design errors, other types of mistakes, and unexpectedcircumstance [20,21].

4. Classification applications in construction

It is important for any application of knowledge sharing tospecifically locate useful information using an IT oriented model. Todevelop such a model, an effective classification model is needed.There are approaches and algorithms for modeling and simulationthat can provide prediction or prevention functions. These approachesare capable of searching, pattern recognition, inference, learning fromexperience, representation, knowledge and reasoning, planning,epistemology, ontology, heuristics, genetic programming, patternclassification, neighbor scaling, best unit matching, and so on. Somemethods utilized in general purpose computing for classification areArtificial Neural Networks (ANN), Self Organizing Map (SOM), FuzzyLogical Control (FLC) and Support Vector Machines (SVM) [22–28].Such studies show that the classification approach is a feasible way toobtain the results needed to effectively resolve particular problems.Chen and Hsu introduced an early warning system for the effects ofchanges in construction orders. They used the CBR approach to extract5 similar cases for use as exemplars to deal with potential disputes[29]. They also developed a hybrid ANN-CBR model to solve theclassification problem for change-order-triggered disputes. Theirclassification accuracy reached 84.61%, proving the practicality ofthe AI and classification approaches [30]. Scholars have also utilizedthe K Nearest Neighbor (KNN) classification in cases related toproviding solutions by classification, learning methods, mapping, andrecognition [31–35]. Of all the above-mentioned algorithms, KNN isthe most widely used for classification due to its simplicity andefficiency [36]. The KNN based approach is capable of dealing withclassification and learning from a massive database to develop aknowledge-sharing model.

5. Research methodology

Serious change-order-triggered disputes in construction projectsmay be resolved by litigation as evidenced by court records. Aknowledge-sharing model must be founded on a database givingcomprehensive details of similar dispute cases. In the U.S.A. it is fromthe circuit court litigation records that we get the best glimpse of thetrial arena, as these records offer the most accessible texts to thepublic. Both written texts and electronic documents are available onwebsites set up by appellate courts (and higher courts). Accordingly, inthis study, we target litigation records filed by appellate and supremecourts, which are available on LEXIS-NEXIS [37]. Through keywordsearching and the criteria set out in Table 1, we found a total of 4818change-order-triggered litigation examples. We spent approximatelyone year to sort and tabulate them. We mention in particular thefollowing factors selected from Tables 1 and 2. Each archive entrygives general information regarding the project, which can answermost questions; see the upper section (project data) in Table 1. Thefirst 6 questions in this section of Table 1 deal with the first 5 factors inTable 2. The 12th and 13th questions in the lower section (disputedissue data) summarize the factor of percent change. The rest of thefactors (Factors No. 7 to 23) in Table 2 refer to the last question inTable 1. However, not every archive entry covers all desired

Table 2Factors summarized from data investigation

Impact factors

No. Factor Description Measured

1 Ownership type Either public or private –

2 Project type Residential, commercial, institutional, industrial, infrastructure, military, waste water or maintenance. –

3 Plaintiff–defendant Either O–G (owner–general contractor/general contractor–owner) or G–S(general contractor–subcontractor/subcontractor–general contractor)

–

4 Contract type Lump sum, GMP, unit price, fixed price, verbal estimate, or others type –

5 Project size Budgeted costs of the projects Dollars6 Percent change The ratio of the change order amount to the project budget amount %7 Additions Additions requested from any one of parties 0–100%8 Deletions Deletions requested from any one of parties 0–100%9 Design change Change in project scope or changes different from the original design 0–100%10 Design error Errors related to design or planning 0–100%11 Design coordination Design coordination among related groups, either owner or contractors 0–100%12 Change code Apply different construction codes 0–100%13 Change techniques Use different construction techniques 0–100%14 Manpower Manpower short, control or other problem related to manpower 0–100%15 Material/equipment Material/equipment available or handling impact 0–100%16 Over-inspection Over-inspection 0–100%17 Rework Did a management problem cause rework? 0–100%18 Scheduling Scheduling extension or compression leads to issue change orders 0–100%19 Cleanup Cleanup request from one of the parties caused changes 0–100%20 Value engineering Value engineering leads to issue of change orders 0–100%21 Unknown conditions Any condition which cannot be predicted or measured ahead of time such as: soil conditions 0–100%22 Weather Severe weather 0–100%23 Others Not catalogued above 0–100%

775J.-H. Chen / Automation in Construction 17 (2008) 773–779

information shown in Table 1. We found complete data for only 340entries, less than 10% of the total, during the investigation. The sum ofthe total percentages for all 340 cases (from Factor No. 7 to No. 23) isalways equal to 100%. With the exception of 4 factors that cannot bequantified for further analyses, it is assumed that the other 19 factorswill have some impact, and that some of these represent significantinfluences leading to litigation.

5.1. Data characteristics

Of all projects that required litigation, residential projects made upthe largest portion, 26.4% of the total. The other 4 most numerousproject types (of a total of 8 types) were commercial, institutional, andinfrastructure projects, representing 17.1%, 14.7%, and 15.9% of thetotal, respectively. Project size was measured in U.S. dollars. Projectsworth less than $200,000 made up the dominant portion at 46.5%;more than 70% of the projects were less than $1 million. No singleproject size exceeded $95 million or was less than $6122. The averageproject size was $2,192,234 with a median of $285,213. The ratio ofprivate to public ownership was 60–40. Most frequently the plaintiffand the defendant would be the owner or general contractor (thefrequency reached 74.1%). With the exception of a small portion (1.8%)the plaintiff–defendants were most frequently construction managersand general contractors. The rest were made up of general contractorsand subcontractors. We also looked at contract type. The investigationshowed that lump-sum contracts made up 88% of the total. Theaverage amount of project changes was 35.4% with the median at17.6%. A certain amount of deviation between the average and medianvalues exists because of the large amount of changes presented fordifference. In the collected data, cardinal changewas shown in only 7%of the total where the maximum amount of change for all projectsreached 470.4% of the original contract price. The major reasons forthese changes were: additions, design changes, and design errors,representing approximately 70% of the total.

5.2. Data analysis

The purposes of data analyses in this study are to show what arethe most significant factors, and to downsize the factors considered asirrelevant or showing correlation. To statistically distinguish the

degree of impact we start with significance testing using the thresholdp-value≤0.05, performed on the basis of the factors listed in Table 2.The results show that only two factors, project size and percentchange, are significant; the rest are considered to have either mediumor low impact. The second type of analysis is based on theclassification concept and is used to identify an optimal decisionrule, designed to minimize the cost function where the cost functioncan be called the expected risk for making this classification decision.To do this, two tasks, factor transformation and dimension reduction,need to be performed to reduce and adjust the factors. They arefundamental in terms of dimension reduction to speed up thecomputing process. All factors for the transformation task, exceptfor 4 non-quantifiable factors, are scaled to be in the range of 0 to 1(0 to 100%); see Table 2. In the dimension reduction task irrelevantfactors are reduced by utilization of the variance-mean ratio andhistograms to check whether the values for each factor remainconstant, nearly constant or dispensable. After examining all factors,the total number of factors can be reduced from 23 to 11. They areproject size (No. 5), percent change (No. 6), additions (No. 7), deletions(No. 8), design change (No. 9), design error (No. 10), design coor-dination (No. 11), change code (No. 12), reworking (No. 17), scheduling(No. 18), and value engineering (No. 20).

5.3. KNN based knowledge-sharing model

To meet the objectives of this study based on the characteristicsof the KNN approaches discussed previously, we first utilizestatistical and mathematical methods to construct the fundamentalframe-work, then develop a pattern classifier to establish themodel. The KNN algorithm used to develop a classifier that must:(1) measure the distance between corresponding elements in eachfeature vector to neighbors assigned by data point features, and(2) distribute the data points to classes based on the nearestneighbors. The features of each data point are recorded in a vector,which contains elements measured as decimals scaled between zeroand one. These elements are used to compute the distance betweenit and neighboring assigned data point. The assumed second degreenorm is used to calculate this distance measurement. There is adefault vector value for each output dimension due to the nature ofthe data. When the vector label of [1 0] appears at the end of these

Table 3Model evaluation

Source of input 340 litigatedcases

32 non-litigatedcases

Classification rate 100% 93.82%Total data number for computation(3-way cross validation)

1020 (340×3) 1116 ((340+32)×3)

Total number of misclassified projects 0 26Misclassification number for litigated projects 0 21Misclassification number fornon-litigated projects

N/A 5

Table 4Data distribution of significant factors for misclassification

Model prototype Model evaluation

Litigated data point Litigated data point Non-litigated data point

Project sizeb$1 million 0 8 1$1–$10 million 0 8 3N$10 million 0 5 1

Percent changeb30% 0 13 330%–90% 0 2 2N90% 0 6 0

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vectors it represents a litigation case and [0 1] represents non-litigation case. The output dimension is consistently set to 2-dimensional. The data points are classified based on the computeddistance and nearest neighbor; however, there is a situation thatrequires special consideration, that is, when the class (neighbor)number is even, it is possible that the distance from the data point tothe class is equal. Such a data point is not assigned to a class and isalso considered a misclassification, the same as when the data pointis assigned to an improper class.

Starting with the database of collected litigation data, we thenutilize the KNN classification method to integrate statistical rulesand probability to fit the modeling demands. To perform KNNclassification, two principle background requirements must besatisfied. First, features need to be assigned two conditions, one forthe background, that requires respective solution of feature selec-tion, and the other is that training samples need to be chosenrandomly from the population. The target labels should also beassigned two conditions. First, each sample is labeled according to itsnature. Second, the labels of the training samples must be known.These two background requirements are needed not only to reachthe objective of pattern classification but to make the model feasibleand flexible. Therefore, let us assume that each data point has its ownvector. To classify an unknown data point p0 the KNN classifier mustrate the neighboring training projects using the cosine distance andclass as follows [38]:

Similarity p1; p2ð Þ ¼ p1 � p2jj p1 jj2jj p2 jj2

; ð1Þ

where p1 and p2 are the neighbors of p0.The similarity of each neighbor can be used to rank the neighbors

of p1 and p2 to p0, that is,

Rank p0;Cð Þ ¼X

Similarity p0;pj� �

D pj;C� �

; ð2Þ

where C={c(i),1≤ i≤M } in the M class and D(pj,C) stands for the clas-sification within the C class. In other words, D(pj,C) can be expressedas

D pj;C� � ¼ 0; pj g C

1; pj a C :

�ð3Þ

Thus, the decision rule in KNN classification can be stated asfollows:

Rule uð Þ ¼ argmaxC

Rank p0;Cð Þð Þ ¼X

Similarity p0; pj� �

D pj;C� �

: ð4Þ

KNN pattern misclassification can be dealt with using some othermathematical methods, specifically, the priori-probability and Bayes'rule. Do this let X be the feature space, where for each x∈X, x is afeature vector randomly selected from X. Assume that the nature-assigned a class label t (x)∈C and P (c(i))=P (x∈c(i)) is the priori-

probability that t (x)∈c(i), without referring to x. Now using Bayes'rule, the pattern classification can be also presented as

P xjc ið Þð ÞP c ið Þð Þ ¼ P c ið Þjxð ÞP xð Þ; ð5Þ

P c ið Þjxð Þ ¼ P xjc ið Þð ÞP c ið Þð ÞPMi¼1

P xjc ið Þð ÞP c ið Þð Þ; ð6Þ

where P (c (i) |x)=P (x∈c(i)|x) is the posteriori-probability that t(x)=c(i);P (x | c (i))=P (x | x∈ c (i)) is the conditional probability that x willassume a value from class c(i); and P(x) is the marginal probabilitythat x will assume a value without referring to which class it belongsto.

The classifier utilizes Eq. (6) to yield the optimal output. Tooptimize Eq. (6), the loss function ℓ (x)and Risk(R) must be definedand minimized. Loss function ℓ (x)and Risk(R) are

S x jg xð Þð Þ ¼ 0 if g xð Þ ¼ t xð Þ1 if g xð Þ p t xð Þ ;

�ð7Þ

Risk Rð Þ ¼RP xð Þ S xjg tð Þð Þdx; ð8Þ

where we design a decision rule g(x)∈C to assign a label to x; and t(x)is the naturally assigned class label.

The misclassification condition that occurs only if g(x)≠ t(x) needsto be considered. A given g(x)=c(i⁎) (instead of using the i describedpreviously as an element in a correct classification). Then

P S x jg xð Þð Þjxð Þ ¼ P S xjg xð Þ ¼ c i⁎ð Þð Þ ¼ 0 jxð Þ¼ P t xð Þ ¼ c i⁎ð Þjxð Þ¼ P c i⁎ð Þjxð Þ:

ð9Þ

Thus, the probability of misclassification for a particular decision is

g xð Þ ¼ c i⁎ð Þ is 1� P c i⁎ð Þjxð Þ: ð10Þ

To minimize the probability of misclassification for a given x, thechoice is to select

g xð Þ ¼ c i⁎ð Þ if P c i⁎ð Þ jxð Þ N P c ið Þjxð Þ for an inot equal to i⁎: ð11Þ

Consequently, in this study the classification rate is defined as theaccuracy of the sorting rate for the distribution of the data points intotheir corresponding correct classes. The higher the classification rateis, the better the KNN pattern classifier of the KNN based knowledgemodel performs. Through the use of 3-way cross validation (whichmeans that the data are equally divided into 3 subsets, two of whichare used for training and the other one is used for testing), the classlabels of k are used to forecast the classes into which the input datapoint should be categorized. The classification rate for this modelusing this database totaled 100%.

Fig. 1. Demonstration of final output.

777J.-H. Chen / Automation in Construction 17 (2008) 773–779

According to the proposed model Eq. (1) is capable of locating aspecified number (defined by the user) of the most similar lawsuitcases for any project. The performing of classification and a search of“state” and “case number” built-up in the model allow for knowledgesharing of past resolution experience.

5.4. Evaluating the KNN based knowledge-sharing model

The classifier of the KNN based knowledge-sharing model isevaluated using data from 32 projects which experienced changes butdid not require litigation. In the evaluation, the data characteristics arediscussed and a comparison of these 32 projects with those in theoriginal database carried out. The non-litigated projects all involvedmechanical construction work. The distribution of their project typeswas quite different from that in the original database. For example

approximately 50% were industrial construction projects, a type ofwork that made up less than 20% of the total work in the database.Residential work comprised the largest portion of work in the originaldatabase, but the smallest portion of the work in the non-litigatedprojects. Commercial work represented the second largest portion ofwork in the database, but it was only 7% of the non-litigated projects.Apart from differences inmeasurement, the non-litigated projects hada similar project size distribution. More than 70% of the projects wereless than 30,000 work-hours. Only a few projects exceeded 100,000work-hours. Among the non-litigated projects, approximately two-thirds were private-sector projects. Also, they mainly used the lump-sum contract type, a finding similar to that for the majority in thedatabase. The largest portion of non-litigated projects had changegreater than 90%, indicating that these projects suffered cardinalchange. This may be usual in the construction industry, but is not

778 J.-H. Chen / Automation in Construction 17 (2008) 773–779

necessarily reflected in the database. Finally, the most commonreasons for changes in the non-litigated projects were additions,design changes, and design errors. These reasons are also consistentwith those in the database; they constitute three-fourths of the non-litigated projects.

After this brief analysis of the non-litigated project data, the KNNbased knowledge-sharingmodel is evaluated based upon entries fromthe tested dataset for these 32 construction projects, following thesteps cited in the previous sections. According to the 3-way crossvalidation concept, the data from the 32 projects are equally andrandomly assigned to the 3 subsets which contain 113–114 pieces oflitigation data and 10–11 pieces of non-litigated project data. Any 2 ofthese 3 subsets can be used as the training data and the other one asthe testing data to yield a classification rate. The process is repeateduntil 3 classification rates are obtained for each subset. The arithmeticmean of these 3 rates is the desired classification rate for projects notexperiencing litigation, and should be as high as possible for thisevaluation. The results in Table 3 show that 21 out of 340 litigationprojects were sortedmistakenly, and 5 out of 32 non-litigated projectswere misclassified. In other words, the input from non-litigatedprojects has only a slight impact on the classification of litigation data,but yields a relatively accurate classification rate. Thus, the modelshows a certain capability for distinguishing between litigated andnon-litigated projects, with accuracy rates of 93.82% and 84.38%,respectively. Table 4 shows the distribution of the misclassification,sorted by the significant factors. There is no conceivable pattern thatexplains the degree of impact of the factors. Fig. 1 shows the mis-classification outputs from the model evaluation and, likewise,provides a knowledge-sharing demonstration.

5.5. Knowledge sharing by using the model

The proposed model is capable of performing classification with ahigh accuracy rate. This means that given the features of the projectchange the model can effectively distinguish where there is potentialfor lawsuits among construction projects facing severe disputescaused by changes. In addition, the process of classification whereina specific number of similar cases are found in the database and thenlinked via LEXIS-NEXIS, means that the model shares information andknowledge of historical adjudication. The reviewing of the examplesof similar past litigation cases may assist the user to resolve disputes.The quantification of information provided by the proposed modelshould speed up knowledge sharing in an even more straightforwardway, for such things as the proportion of change, the weighting of thereasons for the change orders, and the number of change ordersissued. Moreover, the model enhances and facilitates knowledgesharing through exposure to both quantifiable and non-quantifiablekey factors. Such information helps the user narrow down the reasonsfor the disputes, the major reasons that people might neglect toperformwork and management, and even how the legal system dealswith construction disputes based on some specific circumstances. TheKNN based knowledge-sharing model helps to achieve a considerablesaving of time in the settling of potential disputes.

6. Conclusions

In this paper we describe the KNN approach to knowledge sharingin the area of the construction disputes triggered by change orders.The proposed model is a way for construction practitioners who facepotential litigationwith limited knowledge to learn from the past, andto provide early warning of the likelihood of severe disputes. In thisstudy we first constructed a database from court records from whichwe developed the KNN based knowledge-sharing model. We utilized340 historical litigation cases not only for the purpose of knowledgesharing, but to provide a certain degree of nearest neighbor basedprediction to those cases which have experienced serve construction

disputes triggered by change orders. Evaluation was done by usingdata from 32 non-litigation construction projects. It was found thatthe KNN based knowledge sharing had a classification rate accuracy of84.38% according. The KNN algorithm allows for pattern classification;users can select the nearest neighbors (the most similar projects)corresponding to their input, to access the database. This knowledgesharing is built on past experience regarding court-litigated disputeresolutions. The files stored in the LEXIS-NEXIS can be accessed by the“state name” and “case number”. The integration of the KNN approachwith the concept of knowledge sharing is a feasible tool for theprevention of possible litigation triggered by change orders disputes.This KNN approach (utilizing 11 impact factors to develop the KNNbased knowledge-sharing model) is a feasible pattern classificationmethod. The model helps to share knowledge of past court resolveddisputes by court. It is to be hoped that given the know-how thedisputants might possibly be able to resolve the disputes without thefinal resort of going to court. This would save both time and moneyand create a win–win situation.

The model described in this paper is just an initial application, andas such, there is still work to be accomplished. In this study we use apercentage to represent the magnitude of change; however, it ispossible that a comparison between addition and omission changeswould give the same amount, but the degrees of impact may bedifferent. Succeeding studies are suggested to explore this difference.With the rapid development of technology, it is anticipated that thismodel could be implemented in construction projects, especially on-site and would be of great benefit to practitioners. A fully automatedsystem integrating wireless technologies with other more efficientalgorithms would be helpful to resolve future construction disputes.

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