A contractor pre-qualification model based on the quality function deployment method

Contractors play a significant role in construction projects and selection of the most appropriate contractor for the project is a critical decision for clients. The pre-qualification of construction contractors is accounted as a very important step in contractor selection for every project. The main purpose of pre-qualification is to identify an array of eligible contractors, which is required for post-qualification steps and further considerations. Although there have been numerous models for contractor pre-qualification, there are inadequate researches to present a contractor pre-qualification model with consideration of the project objectives or the project owner’s requirements. A new contractor pre-qualification model was developed with the aim of resolving this issue. This model employs the quality function deployment (QFD) method, and considers both the project owner’s requirements and the contractor’s abilities. The results reveal that consideration of the project objectives or the project owner’s requirements and expectations can influence contractor pre-qualification.     

A support vector machine model for contractor prequalification

In complex and high value projects, prequalification is crucial for both contractors and clients, as it targets towards best value delivery through qualification safeguards and streamlined competition among potential candidates. Due the complex nature of the procurement problems such as prequalification exercises, the robust models are rarely attempted. The research reported in this paper presents an overview of potential suitability of Support Vector Machine (SVM) method for contractor/consultant prequalification transactions in the construction project procurements. Furthermore, the performance of SVM is compared with specific artificial neural network outcomes. The results obtained from practical datasets indicate encouraging potentials for SVM applications in the procurement problems such as prequalification and contractor selection. Hence, a SVM-based decision support framework is proposed.     

Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques

The construction project is subject to several risks, one of the most important of which is contractor default because contractor default may increase the final project cost considerably. In the US construction industry, owners commonly shield themselves from the risk of contractor default by transferring this risk to the contractor, who in turn transfers this risk to a surety company. On the other hand, the General Directorate of Military Works (GDMW) of the Kingdom of Saudi Arabia retains the risk of contractor default rather than transferring it to a third party. An artificial neural network (ANN) and a genetic algorithm (GA) are used in this study to predict the risk of contractor default in construction projects undertaken for the Saudi armed forces. Based on this prediction, the Saudi GDMW can make a decision to engage or not to engage the services of a contractor. In case the models are not able to generate reliable predictions (or generate contradictory outcomes), the GDMW will have to augment its budget with contingency funds to be used in the event of contractor default. The outcome of this study is of particular relevance to construction owners because it proposes an approach that can allow them to replace an indiscriminate blanket policy by a policy that is rational, effective, prudent and economical.     

Using the principal component analysis method as a tool in contractor pre‐qualification

Contractor pre‐qualification can be regarded as a complicated, two‐group, non‐linear classification problem. It involves a variety of subjective and uncertain information extracted from various parties such as contractors, pre‐qualifiers and project teams. Non‐linearity, uncertainty and subjectivity are the three predominant characteristics of the contractor pre‐qualification process. This makes the process more of an art than a scientific evaluation. In addition to non‐linearity, uncertainty and subjectivity, contractor pre‐qualification is further complicated by the large number of contractor pre‐qualification criteria (CPC) used in current practice and the multicollinearity existing between contractor attributes. An alternative empirical method using principal component analysis (PCA) is proposed for contractor pre‐qualification in this study. The proposed method may alleviate the existing amount of multicollinearity and largely reduce the dimensionality of the pre‐qualification data set. The applicability and potential of PCA for contractor pre‐qualification has been examined by way of two data sets: (1) 73 pre‐qualification cases (37 qualified and 36 disqualified) collected in England and (2) 85 (45 qualified and 40 disqualified) pre‐qualification cases relating to 10 public sector projects in Hong Kong. The PCA‐based results demonstrated that strong and positive inter‐correlations existed between most of the qualifying variables, with the minimum correlation coefficient being 0.121 and the maximum being 0.899, and that qualified and disqualified contractors could be satisfactorily separated.     

高铁建设管理中危岩落石治理管控博弈分析

我国高铁项目建设中的危岩落石治理管控问题越来越受到参建各方的重视。从建设方的角度对高铁项目建设中危岩 落石治理管控的主要任务进行了阐述,分析了高铁建设管理各阶段参建各方的行为对危岩落石治理管控的影响,建立并求解 了建设方、设计方与咨询方的三方博弈模型,得出不同参数关系下的纳什均衡解,说明在项目建设前期至施工图设计阶段, 建设方制定办法,加大惩罚力度,加强对关键性工程的检查,提高监督成效,会减少设计方、咨询方不履职尽责的概率,有 利于危岩落石问题的根本解决。通过分析,对建设方采取适宜的策略督促各参建单位在危岩落石治理方面尽职尽责提出了具 有管理学意义的建议。     

只具有设计能力工程总承包商酌纵向一体化研究

只具有设计能力的工程总承包商要实施工程总承包项目,对自己目前缺乏能力完成的施工工作必须决策是分包给施工企业还是培育自身的施工能力。作者根据建筑业上下游企业之间的关系,建立了一体化和非一体化两种情况下行业和总承包商的成本模型,得出了行业和总承包商进行纵向一体化的影响因素和条件,并就建筑施工市场的竞争激烈程度对总承包商一体化决策的影响进行了分析,最后以具体的数据作为算例对研究所得到的结论进行了验证。     

Applying cluster analysis to construction contractor classification

The essential task of prequalifying contractors most often involves a large number of firms, each being represented by many disparate dimensions. Therefore, to effectively perform prequalification normally requires an inordinate amount of resource commitment by the construction owner. The statistical technique of cluster analysis (CA) could aid this decision process by classifying contractors into groups of like nature or common characteristics/ability. Further, the technique can identify the most discriminating criteria involved in achieving such classification and, thereby, help avoid subjective rejection of “good” firms, when large numbers of contractors are being considered. Example applications of the CA method are presented, in a construction contractor prequalification scenario.     

EQUAL: a case-based contractor prequalifier

Many researchers have observed that contractor prequalification relies heavily on expert judgement. Experiential judgement is required in decision situations when it would be too costly to measure a large amount of information quantitatively and it is also invaluable in providing shortcuts for complex tasks. However, the quality of heuristic knowledge depends upon the experiences of the decision-makers. The development and use of a case-based reasoning (CBR) system can help decision-makers to produce more reliable and expeditious decisions for contractor prequalification. An Expert preQUALifier — EQUAL — has been developed using the CBR approach. This paper describes the components of EQUAL and illustrates the functionality and operation of the system through a trial run with hypothetical cases. The results of testing reveal that experts were satisfied with the accuracy and overall performance of EQUAL, and CBR approach is suitable for modeling the contractor prequalification domain.     

Fairness in interorganizational project relations: norms and strategies

There is a strong preference for fairness in human interaction, so that people who experience unfairness tend to react with anger, resentment and loss of motivation. Concerns to appear fair influence the behaviour of both individuals and firms. Perceptions of fairness are susceptible to framing and may be influenced by various norms for outcome distribution as well as by decision processes and interpersonal relations. This paper deals with causes and effects of fairness perceptions in construction project relations, mainly fixed price contracts procured by competitive tendering. In such projects, uncertainty results in continuous post contract award problem‐solving and negotiations, and fairness concerns may have incremental but significant influence on the terms of exchange. Case studies of client–contractor interaction in two projects are used to discuss of how fairness norms relate to strategies and industry culture. It is concluded that that an intuitive cost‐based norm of fair pricing shapes interaction in construction projects, but that consequences vary between projects. The norm may favour contractors, but is also related to poor risk management and client distrust. To improve performance, clients need to design procurement practices and communication so that perceptions of contractor losses are counteracted.     

Classifying construction contractors

It is widely accepted in construction management literature that superlative contractor selection criteria are: contractor ability to complete a project on time, within budgeted cost and to expected quality standards. Hence, contractor evaluation and selection models with the ability to highlight these attributes (i.e. help the selection decision) should be fully exploited. To date, such models have evolved based predominantly on reasoning, and discriminant analysis, but there is scope for investigation of alternative strategies including: fuzzy set theory; neural networks; regression techniques; and cluster analysis. This paper concentrates on the latter by applying cluster analysis to real-life contractor selection data. Results indicate that the technique will simultaneously classify large numbers of contractors while identifying the most significant multi-attribute analysis, case-based discriminating criteria among them. These characteristics offer potential for rationalization of contractor evaluation, classification and selection. La documentation relative a la gestion des chantiers de construction admet un certain nombre de criteres de pointe permettant de selectionner les entreprises de batiment, qui sont la capacite de l'entrepreneur a achever un chantier dans les delais, la maitrise des couts prevus au budget et la conformite avec les normes de qualite exigees. En consequence, les modeles d'evaluation et de selection des entreprises du batiment repondant le mieux a l'ensemble de ces criteres (c'est-a-dire, les outils d'aide a la decision) doivent etre pleinement exploites. Ces modeles ont aujourd'hui evolue; ils reposent essentiellement sur des analyses multi-criteres, le raisonnement est fonde sur les cas pratiques et l'analyse distinctive, cependant, ils laissent une large place a la recherche de strategies alternatives, telles que la theorie des ensembles flous, les reseaux neuronaux, les techniques de regression et l'analyse par groupes. Ce document traite plus particulierement de cette derniere strategie, en appliquant l'analyse par groupes aux donnees effectives de selection des entreprises du batiment. Les resultats font apparaitre que cette technique va classer simultanement un grand nombre d'entreprises du batiment, tout en identifiant parmi elles les criteres de jugement les plus significatifs. Ces caracteristiques offrent la possibilite de rationaliser l'evaluation, la classification et la selection des entreprises.     

Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine

This paper investigates structural reliability analysis with both random and interval variables, which is defined as a three‐classification problem and handled by support vector machine (SVM). First, it is determined that projection outlines on the limit‐state surface are crucial for describing separating hyperplanes of the three‐classification problem. Compared with the whole limit‐state surface, the region of projection outlines are much smaller. It will be beneficial to reduce the number of update points and the computational cost if SVM update concentrates on refining the approximate projection outlines. An adaptive local approximation method is developed to realize that the initial built SVM model is sequentially updated by adding new training samples located around the projection outlines. Using this method, the separating hyperplanes can be accurately and efficiently approximated by SVM. Finally, a new method is proposed to evaluate the failure probability interval based on Monte Carlo simulation and the refined SVM.     

Factors affecting the bid/no bid decision in the Saudi Arabian construction contractors

The bid/no bid decision requires an understanding of a company's assessment in relation to factors affecting the decision. Different companies might have different assessment values. The aim is to investigate how bid/no bid decisions are influenced by different characteristics of contractors. Various factors are identified and then analysed in order to investigate their influence and relative significance. A questionnaire survey was used to identify and rank the factors affecting the bidding decision and then analyse them in terms of differences between the returned responses with respect to the differing respondent characteristics. The findings have established the ranking order of the factors affecting the bid/no bid decision and identified their weights of importance. In addition, the influence of these characteristics upon the different weights of importance given by the survey respondents is found to be statistically significant. The most influential characteristics that affected their assessment of the weight of importance are contractor size, classification status of the contractor and the main client type. Different contractors' characteristics should be reflected in the way that the bid/no bid decisions are modelled. Also, the data collected should be categorized with regards to contractors' characteristics before starting the data analysis and modelling processes.     

Contractor selection using the analytic network process

Contractor selection is one of the main activities of clients. Without a proper and accurate method for selecting the most appropriate contractor, the performance of the project will be affected. The multi‐criteria decision‐making (MCDM) is suggested to be a viable method for contractor selection. The analytic hierarchy process (AHP) has been used as a tool for MCDM. However, AHP can only be employed in hierarchical decision models. For complicated decision problems, the analytic network process (ANP) is highly recommended since ANP allows interdependent influences specified in the model. An example is demonstrated to illustrate how this method is conducted, including the formation of supermatrix and the limit matrix.     

A decision model proposal for construction contractor selection in Turkey

This study proposes a multi-criteria decision model for construction contractor selection in the Turkish public sector. Current selection methods are reviewed and three main concepts are generated for selection: cost, time, and quality. The proposed selection model uses evaluation criteria related to these concepts and has a process with two main stages: contractor prequalification and the choice of the eligible (compromised) bidder among prequalified contractors. This model can be used as a decision support system by project owners in order to identify an eligible contractor to be awarded the contract.     

Innovative construction and the role of boundary objects: a Gehry case study

Physical objects have long been used in addressing the challenges involved in constructing innovative buildings, yet their significance for collaborative problem solving in inter-organizational projects is rarely acknowledged. The aim of this research is to investigate what happens when a project team has to collaboratively innovate to address radical design challenges in a construction setting. We focus on the role of a full-scale mock-up of a façade in transforming the design intent for a building by Frank Gehry into design realization. The concept of boundary objects is used as an analytical lens via a case study methodology utilizing non-participant observation of weekly meetings and workshops over a period of 10 months covering client, consultant and contractor involvement. The research shows the role of mock-ups in radical construction settings is in tension along three delivery dimensions: performance, aesthetic and technical construction. Task completion competed with the requirements for experimentation around innovative problem solving with the how to construct it problem left unresolved. The findings suggest that co-location and synchronicity are critical conditions for collaborative and innovative problem solving in radical construction contexts. Project teams need to create open-ended ‘moments’ for iterating critical objects and the interactions that take place around them.     

Forecasting contractor's deviation from the client objectives in prequalification model using support vector regression

Contractor prequalification (CP) is a very complex decision-making process with nonlinearity, uncertainty and imprecision in inputs containing both subjective and objective data. The failure to perform CP can lead to large losses, delays or severe loss of project quality. Although the most reliable approach identified in the literature is currently artificial neural network (ANN), it has weaknesses that negatively affect CP. In this study, a new approach called support vector machines (SVM) has been used to forecast a contractor's deviation from a client's objectives. In order to test the model, CP for 250 virtual contractors was solved. The proposed model had a great generalization in linear, nonlinear, noisy and inductive environments. The Results showed that SVM could reliably perform even with a small amount of training data. Also when compared to ANN, SVM showed an overall better performance.     

IPD项目团队业主方与承包方信任关键影响因素研究

随着项目交付模式的不断优化,IPD模式作为一种最大程度提高效率、改善项目绩效的新型项目交付模式应运而生。由于我国IPD项目团队业主方与承包方之间信任问题较为突出,进而影响了团队的合作,阻碍了IPD模式在我国的应用。通过文献分析法梳理出业主方与承包方之间的信任影响因素,再运用因子分析法得出业主方与承包方的信任关键影响因素,主要是问题解决机制、团队的公平性和声誉。针对这两大核心参与方之间信任关系的建立提出有效的建议,从而达到突破信任障碍、形成良好合作氛围的效果。     

膨胀土等级判别的遗传支持向量机多分类方法

针对支持向量机模型中的参数难以确定的状况,提出了遗传支持向量机方法,即利用遗传算法来搜索支持向量机与核函数的参数,避免了人为选择参数的盲目性,同时提高了支持向量机的推广预测能力,并将该方法应用于膨胀土胀缩等级的判别分类问题。考虑影响膨胀土判别的重要因素,选用液限、胀缩总率、塑性指数、天然含水量和自由膨胀率5个指标作为模型的判别因子,以4类膨胀土胀缩等级作为相应的输出,以膨胀土实测数据作为学习样本进行训练,建立相应分类函数对待判样本进行分类。研究结果表明：遗传支持向量机模型分类性能良好,预测精度高,是膨胀土     

工程质量风险的经济学解释与风险防范

工程项目各参与方在不同阶段均面临不同的质量风险,并各自采取不同的质量风险管理决策。首先从新古典经济学角度,基于利益分歧对业主和承包商在工程质量风险管理方面决策进行经济学解释。建设项目业主与承包商之间、政府与项目法人之间的经济关系集中反映在信息的不对称性与契约的不完备性,从信息经济学角度分析建设项目委托代理关系的规律和基本特征,基于委托—代理关系对质量风险管理决策进行经济学解释。从经济学的角度提出了防范工程质量风险的措施。     

A fuzzy neural network approach for contractor prequalification

Non-linearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which lead to the process being more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eightyfive cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractors' ranking orders, the model efficiency (R²) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The fuzzy neural network is a practical approach for modelling contractor prequalification.