Predicting completed project cost using bidding data

Neural network and regression models have been developed to predict the completed cost of competitively bid highway projects constructed by the New Jersey Department of Transportation. Bid information was studied for inclusion as inputs to the models. Data studied included the low bid, median bid, standard devi9 ation of the bids, expected project duration and the number of bids. A natural log transformation of the data was found to improve the linear relationship between the low bid and completed cost. The stepwise regression procedure was applied, and yielded the best performing predictive model. This regression model used only the natural log of the low bid as independent variable to predict the natural log of the completed cost. Radial basis neural networks were also constructed to predict the final cost. The best performing regres4 sion model produced superior predictions to the best performing neural network model. Hybrid models that used a regression model prediction as an input to a neural network were also studied and were found to also produce reasonable predictions. The calculated models produced good predictions of the completed project cost, but were found to be deficient in predicting very large cost increases. Simple models using the natural log of the low bid as input produced the best results. From the analysis it may be concluded that additional information about the variability of the bids submitted does not provide useful information for predicting the final project outcome.     

A method to determine minimum contract bids for incentive highway projects

An increasing number of state highway agencies (SHAs) are using Incentive/Disincentive (I/D) bidding for highway construction. The I/D bidding concept is designed to shorten the total contract time by allowing the contractor to obtain incentives for early completion and pay disincentives for late completion of a project. Contractors are then presented with the problem of determining a better strategy to develop their bid estimate, including construction cost, construction time, and I/D. This research offers a quantifying model to determine an optimum low bid to submit for both linear I/D and escalating I/D type projects. A functional relationship between the construction cost and time duration is developed from the Florida Department of Transportation (FDOT) projects. The equation is then combined with the I/D to determine the optimum low bid price and time. Finally, several projects completed by the FDOT will be utilized to illustrate the validity of this model.     

项目管理单位中标项目的投资控制与风险管理

项目实施阶段实行代建单位招标的管理模式,是一种全新的项目建设管理模式,项目管理单位以最低价中标并实行中标价包干。根据招标文件要求,中标项目在实施阶段的投资额原则上不认可变更,这对项目的投资管理提出了更高的要求,也给投资控制提出了新的课题。本文着重介绍代建单位的中标项目如何有效控制投资及如何加强风险管理,旨在为代建单位的投资管理提供参考。     

Predicting Cost Escalation Pathways and Deviation Severities of Infrastructure Projects Using Risk‐Based Econometric Models and Monte Carlo Simulation

In the past decade, infrastructure‐related legislation in the United States has consistently emphasized the need to measure the variation associated with infrastructure project cost estimates. Such cost variability is best viewed from the perspective of the project development phases and how the project cost estimate changes as it evolves across these phases. The article first identifies a few gaps in the cost overrun literature. Then it introduces a methodology that uses risk‐based multinomial models and Monte Carlo simulation involving random draws to predict the probability that a project will follow a particular cost escalation pathway across its development phases and that it will incur a given level of cost deviation severity. The article then uses historical data to demonstrate how infrastructure agencies could apply the proposed methodology. Statistical models are developed to estimate the probability that a highway project will follow any specific cost escalation pathway and ultimately, a given direction and severity of cost deviation. The case study results provided some interesting insights. For a given highway functional class, larger project sizes are associated with lower probability of underestimating the final cost; however, such a trend is not exhibited by very large projects (total cost exceeding $30M). For a given project size, higher class roads were generally observed to have a lower probability of underestimating the final cost, compared to lower class roads and this gap in probability narrows as the project size increases. It was determined that a project's most likely pathway of cost escalation is not a guarantee that it will yield any particular direction of cost deviation. The case study results also confirmed the findings of a few past studies that the probabilities of cost escalation pathways and the cost overruns differ significantly across highway districts, and attributed this to differences in administrative culture and work practices across the districts. Infrastructure managers can use the developed methodology to identify which projects are likely to experience a particular pathway of cost escalation, the direction and severity of cost deviation, and to develop more realistic project contingency estimates.     

The accuracy of preliminary cost estimates in Public Works Department (PWD) of Peninsular Malaysia

This paper presents the accuracy of preliminary cost estimates prepared by Public Works Department (PWD), which is the largest public construction management in Malaysia. It attempts to understand the Quantity Surveyors' (QS) estimation accuracy in relation to public projects. This study analyses 83 projects of estimates and tender bids. The analysis includes three (3) estimating targets i.e. lowest bid, accepted bid and mean of the bids. To broaden the study, 344 QS involved in the procurement answered the questionnaires. Linear multiple regression analysis on project characteristics shows that project size, number of bidders, location and type of schools affect the bias. Contract period affects the consistency. The use of mean of the bids is the best-fit target to explain the bias in terms of adjusted R². The accuracy may improve if sufficient design information is available, proper cost planning and improving the application of historical cost data. As an alternative, the phenomenon of overestimation is resulted from government directive instruction, which could challenge the rational of accurate estimate.     

A methodology for scheduling within-day roadway work zones using deep neural networks and active learning

City infrastructure agencies routinely implement road projects that address various elements of urban infrastructure. The majority of these projects are short-term in nature (e.g., utility repair), as they are completed in a few hours within 8:00 a.m. to 5:00 p.m. of a workday. The implementation of these projects during working hours, in spite of the inconvenience imposed on road users, helps the agency avoid extra labor costs associated with nonregular working hours. Careful scheduling of these projects can prevent unduly increased travel delays (road users’ interest) while keeping project costs low (the agency's interest). This study presents a bi-level framework for scheduling short-term urban road projects that analyzes the implicit tradeoffs between the two stakeholders’ interests. The upper-level model establishes the optimal schedule considering the project characteristics, such as cost and duration. The lower-level model captures the dynamic user equilibrium conditions that yield the road users’ path and departure time choices. The bi-level model is a mixed-integer program with nonlinear constraints. Recognizing the relatively low efficiency of traditional solution methods, this paper proposes a deep-neural-network-ensemble-assisted active learning (DN2EA2L) algorithm and adopts a fixed-point algorithm for solving the bi-level model. The numerical experiment uses the Sioux Falls network to demonstrate the efficiency of the DN2EA2L, compared to conventional metaheuristic methods. It is shown that travel time increases due to the project implementation during the peak hours can outweigh the agency's saving in wage costs. Further, it is shown that a significant reduction in the road users’ value of time enables the agency to schedule more projects during regular working hours.     

A decision support system for selecting the optimal contracting strategy in highway work zone projects

Highway work zone projects are challenging for state highway agencies and contractors as they are often located in urban areas and impact local traffic, business community, and neighborhood leading to a multi-party involvement. There is a dynamic relationship between the involved parties and the performance of any highway work zone project is governed by this dynamic relationship. This paper presents a decision support system to assist state Departments of Transportation in selecting suitable contracting strategies for highway work zone projects by considering, at a macro level, the interrelationships between the stakeholders as well as the critical factors impacting the project. The proposed methodology supplements the current project decision-making process with regard to important project performance variables such as cost, schedule, quality, safety, and public satisfaction.     

Putting context to numbers: a geotechnical risk trajectory to cost overrun extremism

The study investigates the cause of the unusually high cost overruns experienced in highway project delivery in the tropical wetland setting of the Niger Delta region of Nigeria. An empirical profiling of cost overrun research reveals the predominance of mono-method studies, all of which are underlain by positivism. The study argues that such positivist philosophies, although methodologically valid, cannot adequately provide in-depth understanding of the contextual cost overrun drivers in highway organisations. Using a mix of methods, the paper examines the contribution of geotechnical risks to the extreme cost overruns experienced on Niger Delta’s highway projects, and demonstrates the relevance of context in cost overrun research. Adopting a geotechnical narrative, data sourced from 61 highway projects along with 16 interviews conducted with highway agencies in the region is analysed. The results of the analysis identified that poor project governance and procurement practices, have inhibited the competent management of geotechnical risk, creating a propensity for extreme cost overruns on highway projects. The study submits the phenomenon of cost overruns in public infrastructure projects is underlain by a complexity of contextual social constructs, which would have been overlooked in positivists studies. Cost overrun research, therefore, needs to be contextually and numerically anchored.     

Integration of Simulation-Based Cost Model and Multi-Criteria Evaluation Model for Bid Price Decisions

Abstract:   Several criteria affect bidding decisions. Current bidding models determine a markup based on a fixed project construction cost. This work presents a novel bid price determination procedure that is built by integrating a simulation-based cost model and a multi-criteria evaluation model. The cost model is used to consider cost uncertainties and generate a bid price cumulative distribution, whereas the multi-criteria evaluation model applies pairwise comparisons and fuzzy integrals to reflect bidder preferences regarding decision criteria. The relationship between the two models is based on a practical phenomenon in that a bidder has a high probability of winning when criteria evaluations favor his bid, and, consequently, the bidder would bid a low price, and vice versa. The merits of the proposed procedure are demonstrated by its application to two construction projects in Taiwan .     

Minimum performance bounds for evaluating contractors performance during construction of highway pavement projects

For project control during construction, evaluating the performance of contractors is usually established through progress measurement that compares the actual performance to the planned performance. Corrective actions and/or performance penalties are then established as relative measures that judge the actual performance based on current project plans without considering the performance of other similar projects or the lowest performance that projects could reach while still being successful. To establish a generalized benchmark measure and a non‐project‐specific project control tool, the concept of minimum performance bounds is explained in relation to their development for highway pavement projects. The bounds were developed using constrained‐parameters polynomial regression and cluster analysis for a sample of 497 highway pavement projects in Washington State. Minimum bounds for small, medium and large projects were fairly distinguishable signifying the project size effect on the location and shape of performance bounds. Bounds were also developed for projects classified by asphalt quantities, contract values, project duration and project length, being the common criteria used by highway agencies. Owners would use the minimum performance bounds as control tools when requesting corrective actions, establishing incentives, imposing performance penalties, initiating a default clause for substandard performance, and/or in pre‐qualifying contractors for new projects.     

A study of preproject planning and project success using ANNs and regression models

It has long been recognized by the industry practitioners that how well preproject planning is conducted has a great impact on project outcome. Through industry project data collection and model analysis, this research intends to investigate the relationship between preproject planning and project success. Preproject planning and project performance information from 62 industrial projects and 78 building projects, representing approximately $5 billion U.S.D. in total construction cost, is collected and used for this research analysis. Based on the information obtained, preproject planning is identified as having direct impact on the project success (cost and schedule performance). Two techniques were then used to develop models for predicting cost and schedule performance: statistical regression analysis, and artificial neural networks (ANNs). The research results provide a valuable source of information that supports better planning in the early stage of the project life cycle and have positive impact on the final project outcome.     

Predicting construction cost overruns using text mining,numerical data and ensemble classifiers

This paper discusses how text describing a construction project can be combined with numerical data to produce a prediction of the level of cost overrun using data mining classification algorithms. Modeling results found that a stacking model that combined the results from several classifiers produced the best results. The stacking ensemble model had an average accuracy of 43.72% for five model runs. The model performed best in predicting projects completed with large cost overruns and projects near the original low bid amount. It was found that a stacking model that used only numerical data produced predictions with lower precision and recall. A potential application of this research is as an aid in budgeting sufficient funds to complete a construction project. Additionally, during the planning stages of a project the research can be used to identify a project that requires increased scrutiny during construction to avoid cost overruns.     

Planning-stage estimation of highway project duration on the basis of anticipated project cost, project type, and contract type

At the planning phase of project development, highway agencies seek to estimate the time duration of project implementation for purposes such as construction planning, contract administration, and workzone impact assessments. This paper investigates the estimation of highway project duration on the basis of variables known at the planning phase such as planned cost and project type, and contract type. Project types are pavement construction, rehabilitation, maintenance, traffic facility installation, and bridge construction. The contract types considered are fixed-duration and fixed-deadline contracts. Using a variety of model specifications, the paper presents mathematical relationships between highway project duration and the magnitude of the planned cost and project type, and contract type. The paper shows that all other factors remaining the same, the duration of fixed-date deadline contracts generally exceed that of fixed-duration contracts; and higher levels of planned cost translate non-linearly into greater project duration. The developed models can help project administrators provide improved estimates of project duration and thus could help reduce project time delays. Also, the models are useful in forecasting workzone durations, a key aspect of workzone user cost estimation.     

Risk‐minimizing approach to bid‐cutting limit determination

Intense price competition is quite common in the construction industry. In many markets, contractors have to cut their bids to compete, giving priority to winning enough contracts to sustain normal operation, and it is common to see a winning bid close to the expected project cost. While cutting bids not only gives up profits but also undoubtedly increases the risk of making a loss, the behaviour of contractors in intense competition is difficult to explain by existing academic bidding models. An approach to determining the lower limit of the bid for a project is proposed based on minimization of the overall loss risk defined by a probabilistic model. The approach can be used to prevent arbitrary over‐cuts in final bid decision where price competition is intense. Factors influencing the suggested bid‐cutting limit for a project are analysed. An illustrative example using real case data is provided.     

关于我国高速公路养护工程造价管理的一些探讨

本文就我国高速公路养护工程造价管理现状进行了分析与阐述,并提出了相关益于提高高速公路养护工程造价管理的方法和途径,以期能对高速公路运营机构在规范养护工程造价管理方面有所帮助。     

Parameter prediction for cash flow forecasting models

The paper describes the application of the DHSS formula to 27 completed construction projects comprising four types - steel-framed low rise buildings, new build housing developments, housing refurbishment projects, and multi-house ‘pre-paint’ maintenance contracts. Application of the formula to individual projects indicates that the ‘best’ parameter values offer a ten-fold improvement over the published values based on project size. Similar results occur when using the best parameter values for other two parameter models.

Various approaches are considered in attempting to predict the best parameter values of the models based on known characteristics of the project. A multiple linear regression with project value, duration, and type independent variables is shown not to produce any significant improvement on standard DHSS formula predictions. However, a reduction in the number of independent variables by cross validation produces an approximately 25% improvement on standard DHSS formula forecasts outside the data base. Examination of the models derived from this analysis indicate the type of project to be of major importance.     

Bidding strategy to support decision-making by integrating fuzzy AHP and regression-based simulation

This study proposes a new bidding strategy to support decision-making that is based on a combined framework of the Fuzzy Analytical Hierarchy Process (FAHP) and regression-based simulation. First, the FAHP method integrates the AHP with fuzzy set theory to determine the weights of factors that influence the cost of a project. Second, the integration of the cumulative distribution functions that are generated by the Monte Carlo simulation with a regression model yields bid amounts that correspond to various confidence levels. The proposed approach is used to analyze data on bridge construction projects that are taken from a database of the Taiwan Public Construction Commission. The systematic bid assessment model and the cost-probability curve can be used as strategic tools for quantifying project risks and calculating bids for construction projects.     

神火集团公司最低价中标法的实践探讨

在论述百分制综合评标法及其缺点的基础上,对河南神火公司在基建招标中采用最低价中标法的经验进行了研究,提出了运用最低价中标法控制工程造价的方法,达到了节省工程造价的结果。     

Hybrid models for global solar radiation prediction: a case study

ABSTRACT

This paper presents a comparison between different prediction models for solar radiation application. The present study assessed the performance of multi-layer perceptron (MLP) as well as boosted decision tree, and used a new combinition of these models with linear regression for the prediction of daily global solar irradiation (DGSR). The performance of the studied models was validated using a real dataset measured at the Applied Research Unit for Renewable Energies (URAER) situated in the south of Algeria. Different input combinations have been analysed in order to select the relevant input parameters for DGSR prediction. The results acheived show that the MLP model perfoms better than the others models in terms of statistical indicators: normalised root mean square error (0.033) and R² (97.7%).