Construction claims management: towards an agent-based approach
Bibliographic record
Abstract
Abstract Disputes are now considered endemic in theconstruction industry. They often arise from the poorresolution of claims in the course of constructionprojects. Efforts have been geared towards reducingthe incidence of claims. These efforts are of two kinds:those that seek answers from basic principles and legalissues at the pre-construction phase and those thatattempt to solve the problems through claimsmanagement procedures at the construction phase.This paper reviews the developments in claimsmanagement and highlights the deficiencies in currentclaims management approaches. It focuses on the needfor improvement of the efficiency of claims negotiationand suggests the use of multiagent systems as anapproach to achieve it. The potential benefits of thesuggested approach are discussed in the concludingsection of the paper.Keywords claim justification, claim negotiation, claimquantification, claims management, multiagent systems,risk management INTRODUCTIONOver the past three decades, the construction industryhas experienced an increase in claims, liability expo-sures and disputes, along with an increasing difficultyin reaching reasonable settlements in an effective,economical and timely manner (Barrie & Paulson,1992). The unique, dynamic and complex nature of theindustry inevitably leads to a situation where conflictsare bound to arise, and claims are inevitable. In fact,claims are now considered as a way of life for theconstruction industry (Bradley & Langford, 1987), asshown by the following:• Onyango (1993) found that 52% of all UK cons-truction projects ended up with a claim of sometype;• Keane (1994) reported that £1.2 billion could be thesubject of construction claims or disputes at any onetime and that more than 83% of contractors claimedfor one or more time extensions during 1992–94 inthe UK;• Semple et al. (1994) identified that more than half ofclaims constituted an additional cost of at least 30%of the original contract value based on their survey ofconstruction projects in Canada. In addition, about athird of claims amounted to at least 60% of theoriginal contract value. In some cases, the claimvalues were almost as high as the original contractvalue.The reasons for this problem are very complex, andcan be analysed from social, industrial and projectperspectives:• Social factors: the construction industry, as a whole,is under increasing pressure from the society to bemore competitive in terms of cost, time, quality andenvironmental issues. As a result, the industry isbecoming more risky than ever;• Industrial factors: the wide range of participants, theincreasing size of projects, enhanced competitivetendering, increasing technological complexity,uncertainty in construction environments, unbal-anced risk allocation, and complex and confusedinterdependent relationships brought about by someproject procurement systems, also contribute toconstruction claims;• Project factors: unforeseeable site conditions, unreal-istic planning and specifications, changes by theclient, acceleration, unfulfilled duties by projectparticipants and ‘force majeure’ are the direct causesfor claims (Ren, 2000).To seek answers to the problem, numerous researchprojects, courses and publications on various aspects ofclaims, such as Wood (1975), Diekmann & Nelson(1985) and Levin (1998), have been undertaken toinvestigate industrial practices and to explore theprinciples and procedures of claim settlement anddispute avoidance. Basically, these efforts are of two
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".