Prediction of outcome of construction dispute claims using multilayer perceptron neural network model
The occurrence of disputes in Indian construction contracts results in damaging the relationship between the parties apart from the time and cost overruns. However, if the parties to a dispute can predict the outcome of the dispute with some certainty, they are more likely to settle the matter out o...
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Veröffentlicht in: | International journal of project management 2015-11, Vol.33 (8), p.1827-1835 |
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creator | Chaphalkar, N.B. Iyer, K.C. Patil, Smita K. |
description | The occurrence of disputes in Indian construction contracts results in damaging the relationship between the parties apart from the time and cost overruns. However, if the parties to a dispute can predict the outcome of the dispute with some certainty, they are more likely to settle the matter out of court resulting in the avoidance of expenses and aggravation associated with adjudication. Dispute resolution process is mainly based upon the facts about the case like conditions of the contracts; actual situations on site; documents presented during arbitrational proceedings, etc., which are termed as ‘intrinsic factors’ in this research. These facts and evidences being intrinsic to the cases have been explored by researchers to develop dispute resolution mechanisms. This study focuses on determining the intrinsic factors for construction disputes related to claims raised due to variation from 72 arbitration awards through Case Study approach and furthermore statistically proving their importance in arbitral decision making by seeking professional cognizance through a questionnaire survey. It also further asserts the feasibility of the multilayer perceptron neural network approach based on the intrinsic factors existing in the construction dispute case for predicting the outcome of a dispute. Data from 204 variation claims from the awards is employed for developing the model. A three-layer multilayer perceptron neural network was appropriate in building this model, which has been trained, validated, and tested. The tool so developed would result in dispute avoidance, to some extent, and would reduce the pressure on the Indian judiciary.
•The study put forth the taxonomy of Intrinsic and Extrinsic factors influencing the decision making of the arbitrators.•The research identified 16 intrinsic factors which influence the arbitral decision making related to variation claims.•Use of neural network model for predicting the outcome for a specific type of claim for disputes in Indian construction contracts has been exploited for the first time through this research. |
doi_str_mv | 10.1016/j.ijproman.2015.09.002 |
format | Article |
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•The study put forth the taxonomy of Intrinsic and Extrinsic factors influencing the decision making of the arbitrators.•The research identified 16 intrinsic factors which influence the arbitral decision making related to variation claims.•Use of neural network model for predicting the outcome for a specific type of claim for disputes in Indian construction contracts has been exploited for the first time through this research.</description><identifier>ISSN: 0263-7863</identifier><identifier>EISSN: 1873-4634</identifier><identifier>DOI: 10.1016/j.ijproman.2015.09.002</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Arbitration ; Artificial intelligence techniques ; Construction contracts ; Construction disputes ; Decision making ; Dispute resolution ; Disputes ; Extrinsic factors ; Intrinsic factors ; Neural networks ; Studies</subject><ispartof>International journal of project management, 2015-11, Vol.33 (8), p.1827-1835</ispartof><rights>2015 Elsevier Ltd and Association for Project Management and the International Project Management Association</rights><rights>Copyright Elsevier Science Ltd. Nov 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-f5043c92852bd825f7629b7fb6e337a19f430444a67735f8846a12edcec503993</citedby><cites>FETCH-LOGICAL-c371t-f5043c92852bd825f7629b7fb6e337a19f430444a67735f8846a12edcec503993</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijproman.2015.09.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Chaphalkar, N.B.</creatorcontrib><creatorcontrib>Iyer, K.C.</creatorcontrib><creatorcontrib>Patil, Smita K.</creatorcontrib><title>Prediction of outcome of construction dispute claims using multilayer perceptron neural network model</title><title>International journal of project management</title><description>The occurrence of disputes in Indian construction contracts results in damaging the relationship between the parties apart from the time and cost overruns. However, if the parties to a dispute can predict the outcome of the dispute with some certainty, they are more likely to settle the matter out of court resulting in the avoidance of expenses and aggravation associated with adjudication. Dispute resolution process is mainly based upon the facts about the case like conditions of the contracts; actual situations on site; documents presented during arbitrational proceedings, etc., which are termed as ‘intrinsic factors’ in this research. These facts and evidences being intrinsic to the cases have been explored by researchers to develop dispute resolution mechanisms. This study focuses on determining the intrinsic factors for construction disputes related to claims raised due to variation from 72 arbitration awards through Case Study approach and furthermore statistically proving their importance in arbitral decision making by seeking professional cognizance through a questionnaire survey. It also further asserts the feasibility of the multilayer perceptron neural network approach based on the intrinsic factors existing in the construction dispute case for predicting the outcome of a dispute. Data from 204 variation claims from the awards is employed for developing the model. A three-layer multilayer perceptron neural network was appropriate in building this model, which has been trained, validated, and tested. The tool so developed would result in dispute avoidance, to some extent, and would reduce the pressure on the Indian judiciary.
•The study put forth the taxonomy of Intrinsic and Extrinsic factors influencing the decision making of the arbitrators.•The research identified 16 intrinsic factors which influence the arbitral decision making related to variation claims.•Use of neural network model for predicting the outcome for a specific type of claim for disputes in Indian construction contracts has been exploited for the first time through this research.</description><subject>Arbitration</subject><subject>Artificial intelligence techniques</subject><subject>Construction contracts</subject><subject>Construction disputes</subject><subject>Decision making</subject><subject>Dispute resolution</subject><subject>Disputes</subject><subject>Extrinsic factors</subject><subject>Intrinsic factors</subject><subject>Neural networks</subject><subject>Studies</subject><issn>0263-7863</issn><issn>1873-4634</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFkEtLxDAUhYMoOI7-BSm4bs2rSbtTBl8woAtdh0x6K6ltU_NQ5t_bMrp2dS7cc87lfghdElwQTMR1V9hu8m7QY0ExKQtcFxjTI7QilWQ5F4wfoxWmguWyEuwUnYXQYUwkLuUKwYuHxppo3Zi5NnMpGjfAMho3hujTYdXYMKUImem1HUKWgh3fsyH10fZ6Dz6bwBuYop-tIySv-1nit_Mf2eAa6M_RSav7ABe_ukZv93evm8d8-_zwtLnd5oZJEvO2xJyZmlYl3TUVLVspaL2T7U4AY1KTuuUMc861kJKVbVVxoQmFxoApMatrtkZXh96Zx2eCEFXnkh_nk4pIWlFJOF9c4uAy3oXgoVWTt4P2e0WwWpCqTv0hVQtShWs1I52DN4cgzD98WfAqGAujmQl6MFE1zv5X8QPTYYTE</recordid><startdate>20151101</startdate><enddate>20151101</enddate><creator>Chaphalkar, N.B.</creator><creator>Iyer, K.C.</creator><creator>Patil, Smita K.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TA</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope></search><sort><creationdate>20151101</creationdate><title>Prediction of outcome of construction dispute claims using multilayer perceptron neural network model</title><author>Chaphalkar, N.B. ; Iyer, K.C. ; Patil, Smita K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-f5043c92852bd825f7629b7fb6e337a19f430444a67735f8846a12edcec503993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Arbitration</topic><topic>Artificial intelligence techniques</topic><topic>Construction contracts</topic><topic>Construction disputes</topic><topic>Decision making</topic><topic>Dispute resolution</topic><topic>Disputes</topic><topic>Extrinsic factors</topic><topic>Intrinsic factors</topic><topic>Neural networks</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chaphalkar, N.B.</creatorcontrib><creatorcontrib>Iyer, K.C.</creatorcontrib><creatorcontrib>Patil, Smita K.</creatorcontrib><collection>CrossRef</collection><collection>Materials Business File</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><jtitle>International journal of project management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chaphalkar, N.B.</au><au>Iyer, K.C.</au><au>Patil, Smita K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of outcome of construction dispute claims using multilayer perceptron neural network model</atitle><jtitle>International journal of project management</jtitle><date>2015-11-01</date><risdate>2015</risdate><volume>33</volume><issue>8</issue><spage>1827</spage><epage>1835</epage><pages>1827-1835</pages><issn>0263-7863</issn><eissn>1873-4634</eissn><abstract>The occurrence of disputes in Indian construction contracts results in damaging the relationship between the parties apart from the time and cost overruns. However, if the parties to a dispute can predict the outcome of the dispute with some certainty, they are more likely to settle the matter out of court resulting in the avoidance of expenses and aggravation associated with adjudication. Dispute resolution process is mainly based upon the facts about the case like conditions of the contracts; actual situations on site; documents presented during arbitrational proceedings, etc., which are termed as ‘intrinsic factors’ in this research. These facts and evidences being intrinsic to the cases have been explored by researchers to develop dispute resolution mechanisms. This study focuses on determining the intrinsic factors for construction disputes related to claims raised due to variation from 72 arbitration awards through Case Study approach and furthermore statistically proving their importance in arbitral decision making by seeking professional cognizance through a questionnaire survey. It also further asserts the feasibility of the multilayer perceptron neural network approach based on the intrinsic factors existing in the construction dispute case for predicting the outcome of a dispute. Data from 204 variation claims from the awards is employed for developing the model. A three-layer multilayer perceptron neural network was appropriate in building this model, which has been trained, validated, and tested. The tool so developed would result in dispute avoidance, to some extent, and would reduce the pressure on the Indian judiciary.
•The study put forth the taxonomy of Intrinsic and Extrinsic factors influencing the decision making of the arbitrators.•The research identified 16 intrinsic factors which influence the arbitral decision making related to variation claims.•Use of neural network model for predicting the outcome for a specific type of claim for disputes in Indian construction contracts has been exploited for the first time through this research.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ijproman.2015.09.002</doi><tpages>9</tpages></addata></record> |
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subjects | Arbitration Artificial intelligence techniques Construction contracts Construction disputes Decision making Dispute resolution Disputes Extrinsic factors Intrinsic factors Neural networks Studies |
title | Prediction of outcome of construction dispute claims using multilayer perceptron neural network model |
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