Prediction of the Performance Related to Financial Capabilities Using Multilayer Perceptron

In construction projects, there are circumstances when contractors meet financial prequalification requirements but show low financial performance in practice. These cases bring about the complexity in contractor selection. Hence, the aim of this research is to build a prediction model that finds contractors' financial performance to support decision makers assess contractors more efficiently in prequalification phase. Thus, this study takes recent roadwork Term Contracts Projects with each with the corresponding contractor's records to train the model to predict Performance related to Financial Capabilities PFC. The Multilayer Perceptron MLP is utilized to find the nonlinear correlation between the PFC and contractors' characteristics. The research finds that more financialcompetitive contractors show less financial performance than less competitive ones. The findings of the research help the client improve the current contractors' evaluation system to exhaust the possibilities of financial performance.

[1]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[2]  Edyta Plebankiewicz.  A fuzzy sets based contractor prequalification procedure , 2012 .

[3]  K. Iyer,et al.  Understanding the role of contractor capability in risk management: a comparative case study of two similar projects , 2020, Construction Management and Economics.

[4]  Sławomir Biruk,et al.  Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment , 2010 .

[5]  Petros Kalantonis,et al.  Investigation of financial distress with a dynamic logit based on the linkage between liquidity and profitability status of listed firms , 2019, J. Oper. Res. Soc..

[6]  Jeffrey S. Russell,et al.  Model for Owner Prequalification of Contractors , 1990 .

[7]  Li-Chung Chao,et al.  Fuzzy model for predicting project performance based on procurement experiences , 2012 .

[8]  Uwe Reuter,et al.  A comparative study of machine learning approaches for modeling concrete failure surfaces , 2018, Adv. Eng. Softw..

[10]  Shouqing Wang,et al.  Optimizing an Equity Capital Structure Model for Public–Private Partnership Projects Involved with Public Funds , 2017 .

[11]  Yiwang Wang,et al.  Development and application of tender evaluation decision-making and risk early warning system for water projects based on KDD , 2012, Adv. Eng. Softw..

[12]  Aminah Robinson Fayek,et al.  COMPETITIVE BIDDING STRATEGY MODEL AND SOFTWARE SYSTEM FOR BID PREPARATION , 1998 .

[13]  T J VanderWeele,et al.  Mediation Analysis with Multiple Mediators , 2014, Epidemiologic methods.

[14]  M. M. Rahman.  Cost of Prequalification: A Pilot Study , 2015 .

[15]  C. M. Tam,et al.  GA-ANN model for optimizing the locations of tower crane and supply points for high-rise public housing construction , 2003 .

[16]  Armin Seyfried,et al.  Prediction of pedestrian dynamics in complex architectures with artificial neural networks , 2020, J. Intell. Transp. Syst..

[17]  Jeffrey S. Russell,et al.  Predicting Contract Surety Bond Claims Using Contractor Financial Data , 1994 .

[18]  Patrick S. W. Fong,et al.  Final contractor selection using the analytical hierarchy process , 2000 .

[19]  Weng Tat Chan,et al.  A type-2 fuzzy set model for contractor prequalification , 2017 .

[20]  Gul Polat,et al.  Mark-up Size Estimation in Railway Projects using the Integration of AHP and Regression Analysis Techniques☆ , 2015 .

[21]  Po-Cheng Chen,et al.  Predicting Construction Contractor Default with Option-Based Credit Models—Models’ Performance and Comparison with Financial Ratio Models , 2011 .

[22]  Hojjat Adeli,et al.  Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes , 2018, Journal of Construction Engineering and Management.

[23]  David Arditi,et al.  Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques , 2005 .

[24]  Mohammed Wanous,et al.  A neural network bid/no bid model: the case for contractors in Syria , 2003 .

[25]  Mostafa Khalifa,et al.  Factors influencing sub-contractors selection in construction projects , 2013 .

[26]  Joseph H. M. Tah,et al.  Information modellng for case-based construction planning of highway bridge projects , 1999 .

[27]  G. Dowling,et al.  Corporate reputation and sustained superior financial performance , 2002 .

[28]  A. Halim Boussabaine,et al.  The use of artificial neural networks in construction management: a review , 1996 .

[29]  Miroslaw J. Skibniewski,et al.  DECISION CRITERIA IN CONTRACTOR PREQUALIFICATION , 1988 .

[30]  Hongwei Wang,et al.  Model of Equipment Sharing between Contractors on Construction Projects , 2018, Journal of Construction Engineering and Management.

[31]  Khaled Mahmoud El-Gohary,et al.  Engineering Approach Using ANN to Improve and Predict Construction Labor Productivity under Different Influences , 2017 .

[32]  Stefan Olander.  Performance-Based Contracts for Road Projects , 2014 .

[33]  David Arditi,et al.  Marketing Practices of U.S. Contractors , 2008 .

[34]  Dan Wang,et al.  Efficacy of Using Support Vector Machine in a Contractor Prequalification Decision Model , 2010, J. Comput. Civ. Eng..

[35]  Hamid Abdirad,et al.  Fuzzy AHP Model for Prequalification of Engineering Consultants in the Iranian Public Procurement System , 2017 .

[36]  Yang Miang Goh,et al.  Neural network analysis of construction safety management systems: a case study in Singapore , 2013 .

[37]  Theophilus Adjei-Kumi,et al.  Ascertaining the impact of contractors pre-qualification criteria on project success criteria , 2019, Engineering, Construction and Architectural Management.

[38]  Konstantinos Morfidis,et al.  Seismic parameters' combinations for the optimum prediction of the damage state of R/C buildings using neural networks , 2017, Adv. Eng. Softw..

[39]  Long Le-Hoai,et al.  Effect of project complexity on cost and schedule performance in transportation projects , 2018, Construction Management and Economics.

[40]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[41]  Min-Yuan Cheng,et al.  Evaluating Contractor Financial Status Using a Hybrid Fuzzy Instance Based Classifier: Case Study in the Construction Industry , 2015, IEEE Transactions on Engineering Management.

[42]  Edwin H.W. Chan,et al.  Factors Influencing Building Contractors’ Pricing for Time-Related Risks in Tenders , 2009 .

[43]  Daniel Jato-Espino,et al.  A review of application of multi-criteria decision making methods in construction , 2014 .

[44]  S. Hsu,et al.  Improving Hedging Decisions for Financial Risks of Construction Material Suppliers Using Grey System Theory , 2017 .

[45]  Samuel Y. L. Yin,et al.  Contractor financial prequalification using simulation method based on cash flow model , 2013 .

[46]  Mehmet Emre Bayraktar,et al.  A decision support system for selecting the optimal contracting strategy in highway work zone projects , 2009 .

[47]  David G. Carmichael,et al.  Comparative performance of publicly listed construction companies: Australian evidence , 2010 .

[48]  Moonseo Park,et al.  Contractor Liquidity Evaluation Model for Successful Public Housing Projects , 2018, Journal of Construction Engineering and Management.

[49]  A. Griffith.  Delivering best value in the small works portfolio of public sector organizations when using preferred contractors , 2011 .

[50]  Jui-Sheng Chou,et al.  Optimized artificial intelligence models for predicting project award price , 2015 .

[51]  Gary David Holt,et al.  Prequalification and multi-criteria selection: a measure of contractors' opinions , 1998 .

[52]  Zaher Mundher Yaseen,et al.  Predicting compressive strength of lightweight foamed concrete using extreme learning machine model , 2018, Adv. Eng. Softw..

[53]  Farnad Nasirzadeh,et al.  Fuzzy Cognitive Map Approach to Analyze Causes of Change Orders in Construction Projects , 2018 .

[54]  Jeffrey S. Russell,et al.  Predicting contractor failure using stochastic dynamics of economic and financial variables , 1996 .

[55]  Florence Yean Yng Ling,et al.  Effects of interpersonal relations on public sector construction contracts in Vietnam , 2012 .

[56]  Helena Lidelöw,et al.  Middle managers’ perceptions of operations strategies at construction contractors , 2019, Construction Management and Economics.

[57]  Zedan Hatush,et al.  Evaluating contractor prequalification data: selection criteria and project success factors , 1997 .

[58]  Abdulaziz A. Bubshait,et al.  Contractor Prequalification in Saudi Arabia , 1996 .

[59]  Amirhosein Jafari,et al.  A contractor pre-qualification model based on the quality function deployment method , 2013 .

[60]  Wei-Chih Wang,et al.  Using data envelopment analysis to support best-value contractor selection , 2015 .

[61]  Ying Gao,et al.  Effect of Level of Owner-Provided Design on Contractor’s Design Quality in DB/EPC Projects , 2019 .

[62]  Matloub Hussain,et al.  Determining success factors in Abu Dhabi health care construction projects: customer and contractor perspectives , 2018 .

[63]  Ahmad Hadavi,et al.  Effects of high prequalification requirements , 1999 .

[64]  M. Buscema,et al.  Introduction to artificial neural networks. , 2007, European journal of gastroenterology & hepatology.

[65]  Peter E.D. Love,et al.  Combining rule-based expert systems and artificial neural networks for mark-up estimation , 1999 .

[66]  Jonathan Jingsheng Shi,et al.  A neural network based system for predicting earthmoving production , 1999 .

[67]  Kumar Neeraj Jha,et al.  Prediction of schedule performance of Indian construction projects using an artificial neural network , 2011 .

[68]  Keith W. Hipel,et al.  Construction contract management using value packaging systems , 2017 .

[69]  Khaled Nassar,et al.  Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors , 2013 .

[70]  Jörgen Modin,et al.  KBS-CLASS: a neural network tool for automatic content recognition of building texts , 1995 .

[71]  Wen-Haw Huang,et al.  Dynamic Threshold Cash Flow–Based Structural Model for Contractor Financial Prequalification , 2014 .

[72]  Mehmet Önder Efe,et al.  A comparison of networked approximators in parallel mode identification of a bioreactor , 2010, Adv. Eng. Softw..

[73]  Li‐Chung Chao,et al.  Estimating project overheads rate in bidding: DSS approach using neural networks , 2010 .

[74]  R. Aziz,et al.  Identification of construction subcontractor prequalification evaluation criteria and their impact on project success , 2019, Alexandria Engineering Journal.

[75]  Jae-Myung Lee,et al.  Corroded pipeline failure analysis using artificial neural network scheme , 2017, Adv. Eng. Softw..

[76]  David Lowe,et al.  Data modelling and the application of a neural network approach to the prediction of total construction costs , 2002 .

[77]  Ammar Peter Kaka,et al.  Artificial neural network cost flow risk assessment model , 2013 .

[78]  Ching-Hung Lee,et al.  Prediction of machining accuracy and surface quality for CNC machine tools using data driven approach , 2017, Adv. Eng. Softw..

[79]  Chao Zhang,et al.  Deep learning enabled cutting tool selection for special-shaped machining features of complex products , 2019, Adv. Eng. Softw..

[80]  Sunil K. Sinha,et al.  Construction equipment productivity estimation using artificial neural network model , 2006 .

[81]  Goh Bee Hua.  Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression , 1996 .

[82]  Amir Hossein Gandomi,et al.  Assessment of artificial neural network and genetic programming as predictive tools , 2015, Adv. Eng. Softw..

[83]  Do Kyun Kim,et al.  A simplified method to predict fatigue damage of TTR subjected to short-term VIV using artificial neural network , 2018, Adv. Eng. Softw..