Time Series Models for Forecasting Construction Costs Using Time Series Indexes

Construction often involves considerable time gaps between cost estimation and on-site operations. In addition, many operations are performed over a considerable period of time. Accordingly, estimating construction costs must consider the trend of costs in the market, where construction costs normally change over time. Insight into the trend of construction costs in the market, therefore, is beneficial, even critical, to the effective cost management of construction projects. In an effort to support such insight development, two time series models were built by analyzing time series index data and comparing them with existing methods in the present study. The developed time series models accurately predict construction cost indexes. In particular, the models respond sensitively and swiftly to a quick, large change of costs, which allows for accurate forecasting over the short- and long-term periods. Overall, the models are effective for understanding the trend of construction costs.     

Estimating Future Highway Construction Costs

The objective of this research was to develop a model that estimates future highway construction costs in Louisiana. The model describes overall highway construction cost in terms of a highway construction cost index. The index is a composite measure of the cost of construction labor, materials, and equipment; the characteristics of contracts; and the environment in which contracts are let. Future construction costs are described in terms of predicted index values based on forecasts of the price of construction labor, materials, and equipment and the expected contract characteristics and contract environments. The contract characteristics and contract environments that are under the control of highway agency officials, can be manipulated to reflect future cost-cutting policies. Application of the model in forecasting to highway construction costs in Louisiana shows that the model closely replicates past construction costs for the period 1984–1997. When applied to forecasting future highway construction costs, the model predicts that highway construction costs in Louisiana will double between 1998 and 2015. Applying cost-cutting policies and assuming input costs are 20% less than anticipated, the model estimates highway construction costs will increase by 75% between 1998 and 2015.     

Reducing the Economic Risk of LNG Tank Construction under Conditions of Fluctuating Resource Prices

Fluctuating resource prices affect the costs of the materials used in the construction of LNG (liquid natural gas) tanks. In this paper, methods based on the field experience of the writers for the reduction of the cost of LNG tanks in general and in-ground LNG tanks in particular are discussed. Of the various components of the construction cost, the price of steel materials is a significant contributor. Using a newly defined cost impact index, the costs of construction of different types of LNG tanks are compared. Further, by considering actual examples of the construction of in-ground tanks, important issues relating to the use of steel materials are identified and, by using a cost reduction index, recommendations are made for the reduction of the cost risk during fluctuations in resource prices.     

Production System Loading–Cycle Time Relationship in Residential Construction

Production home building possesses characteristics similar to manufacturing processes, such as the construction of more or less similar houses repeatedly and a growing demand for mass customization of homes. As a result of these similarities, larger homebuilders often attempt to view their production system as an assembly line process. However, the management tools generally utilized by these home builders are those used in other sectors of the construction industry, such as critical path method scheduling, cost estimating, and earned value analysis. These management tools do not provide an explanation or control/prediction tools for many undesirable situations that arise during home building, such as increasing cycle time which slows delivery of product to consumers and increases project capital costs, and increasing amounts of work in process that increases capital investment and thereby decreases company financial performance. In order to bring better management tools to the residential construction industry, this study examines relationships between cycle time, work in process, system throughput, new construction starts, and the capacity of the production system using building permit data for new single family homes in Chandler, Ariz. The applicability of Little’s law, a basic equation used in factory production management models, to a residential production system is examined. This study shows a definite, predictable relationship between cycle time, work in process, and production system throughput. It provides a pathway for further study of production system characteristics that have historically not been included in construction management models, with the expectation of developing new construction management tools that will account for more of the characteristics of construction production systems that affect project performance and company financial performance.     

An Ontology for Relating Features with Activities to Calculate Costs

It is the cost estimator’s task to determine how the building design influences construction costs. Estimators must recognize the design conditions that effect construction costs and adjust the project’s activities, resources, and resource productivity rates accordingly to create a cost estimate for a particular design. Current tools and methodologies help estimators to establish relationships between product and cost information to calculate quantities automatically. However, they do not provide a common vocabulary to represent estimators’ rationale for relating product and cost information. This paper presents the ontology we formalized to represent estimators’ rationale for relating features of building product models to construction activities and associated construction resources to calculate construction costs. A software prototype that implements the ontology enables estimators to generate activities that know what feature requires their execution, what resources are being used and why, and how much the activities’ execution costs. Validation studies of use of the prototype system provide evidence that the ontology enabled estimators to generate and maintain construction cost estimates more completely, consistently, and expeditiously than traditional tools.     

Louisiana Highway Construction Cost Trend after Hurricanes Katrina and Rita

The objective of this study was to reveal the trend in highway construction costs following Hurricanes Katrina and Rita in Louisiana. The means of measuring highway construction cost was the Louisiana Highway Construction Index, an index made up of the cost of labor, equipment, and six major materials used in highway construction. Data from projects let by the Louisiana Department of Transportation and Development from the second quarter of 2003 to the second quarter of 2007 were used to track the change in construction costs. Index values from hurricane-impacted areas (GO Zones) were compared with those in Non-GO Zones. The indices revealed that two quarters after Hurricanes Katrina and Rita, the highway construction cost jumped about 20% statewide and 51% in GO Zone. Two years after the hurricanes, the cost has stabilized to around 30% increase over the pre-Katrina and Rita period. This study provides valuable information for the state agency to estimate cost escalation in ongoing projects and to estimate future disaster response to highway construction costs.     

Single versus Multiple Prime Contracting

Policies regarding public building construction affect the interests of taxpayers, construction authorities, general contractors, specialty contractors, and other stakeholders. At the state level, the debate as to the optimal form such policies should take has become an ongoing struggle among competing interests. This study presents a systematic analysis of the main issues regarding single versus multiple prime contracting with the purpose of providing objective data to illuminate the debate. A statistical analysis of project bids and final costs from a national sample of state construction projects reveals that public construction projects organized with multiple prime contracts tend to have 5% less direct costs than projects using a single prime contractor. Moreover, approximately 80% of these savings are attributable to lower bid costs for multiple prime projects. The results of this study are in agreement with theoretical bidding models and efficient risk allocation models. Theoretical bidding models suggest that, in the absence of disruptions, multiple prime projects should have lower direct costs than single prime jobs. Efficient cost allocation models suggest that when specialty contractors do not bear the risks associated with the single prime contracting method (e.g., bid shopping and payment delays) they are willing to lower their bids, and forego the premium they would normally charge in response to such risks, as seems to be the case in multiple prime jobs.     

Neural Network Modeling of Highway Construction Costs

The objective of this research was to develop a procedure that estimates the escalation of highway construction costs over time. An artificial neural network model was developed which relates overall highway construction costs, described in terms of a highway construction cost index, to the cost of construction material, labor, and equipment, the characteristics of the contract and the contracting environment prevailing at the time the contract was let. Results demonstrate that the model is able to replicate past highway construction cost trends in Louisiana with reasonable accuracy. Future construction input costs are estimated from commercially available forecasts of indicator variables closely associated with the price of construction labor, construction equipment, and a representative set of highway construction materials. Future contract characteristics and the contracting environment that is likely to exist in the future are estimated from past trends or stipulated to be consistent with policy decisions in the future. The predictions produced by the model estimate that highway construction costs in Louisiana will double between 1998 and 2015.     

Stochastic Time-Cost-Resource Utilization Optimization Using Nondominated Sorting Genetic Algorithm and Discrete Fuzzy Sets

In a construction project, the cost and duration of activities could change due to different uncertain variables such as weather, resource availability, etc. Resource leveling and allocation strategies also influence total time and costs of projects. In this paper, two concepts of time-cost trade-off and resource leveling and allocation have been embedded in a stochastic multiobjective optimization model which minimizes the total project time, cost, and resource moments. In the proposed time-cost-resource utilization optimization (TCRO) model, time and cost variables are considered to be fuzzy, to increase the flexibility for decision makers when using the model outputs. Application of fuzzy set theory in this study helps managers/planners to take these uncertainties into account and provide an optimal balance of time, cost, and resource utilization during the project execution. The fuzzy variables are discretized to represent different options for each activity. Nondominated sorting genetic algorithm (NSGA-II) has been used to solve the optimization problem. Results of the TCRO model for two different case studies of construction projects are presented in the paper. Total time and costs of the two case studies in the Pareto front solutions of the TCRO model cover more than 85% of the ranges of total time and costs of solutions of the biobjective time-cost optimization (TCO) model. The results show that adding the resource leveling capability to the previously developed TCO models provides more practical solutions in terms of resource allocation and utilization, which makes this research relevant to both industry practitioners and researchers.     

Time Series Analysis of ENR Construction Cost Index

Every month, Engineering News-Record (ENR) publishes the construction cost index (CCI), which is a weighted aggregate index of the 20-city average prices of construction activities. Although CCI increases over the long term, it is subject to considerable short-term variations, which make it problematic for cost estimators to prepare accurate bids for contractors or engineering estimates for owner organizations. The ability to predict construction cost trends can result in more-accurate bids and avoid under- or overestimation. This paper summarizes and compares the applicability and predictability of various univariate time series approach for in-sample and out-of-sample forecastings of CCI. It is shown that the seasonal autoregressive integrated moving-average model is the most-accurate time series approach for in-sample forecasting of CCI, while the Holt-Winters exponential smoothing model is the most-accurate time series approach for out-of-sample forecasting of CCI. It is also shown that several time series models provide more-accurate out-of-sample forecasts than the ENR’s subject matter experts’ CCI forecast. Cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-accurate bids for contractors and budgets for owners. Owners and contractors can use CCI forecasting in reducing construction costs by better-timed project execution.     

CBR Revision Model for Improving Cost Prediction Accuracy in Multifamily Housing Projects

For any construction project to succeed, it is very important to accurately estimate the construction cost during the project’s initial stage. This is why there has been much interest lately in cost prediction models that use case-based reasoning (CBR). It has been pointed out, however, that existing CBR-based cost prediction models may yield inaccurate results even though they could survey optimal similar cases, if the number of cases in the case base is not enough. As opposed to the existing CBR-based construction cost prediction models, this study developed a CBR revision model that reflects the “revise” phase of the CBR cycle (retrieve, reuse, revise, and retain) based on nine multifamily housing projects executed recently by “A” Housing Corporation. To verify the developed model, a case study was performed using three case projects completed by “B” and “C” Housing Corporations. The result showed that the prediction error ratio after the Revise (I) phase decreased from 97.44 to 22.58%. This model can be effective when there are insufficient established cases in the case base.     

Tailoring Competitive Advantages Derived from Innovation to the Needs of Construction Firms

Innovation is often classified as a cost intensive investment in the construction industry with indefinite returns. Due to the clients’ tendency to award projects based on the lowest costs, innovation is often seen as an unfeasible strategy toward the competitiveness that construction firms are seeking. This study questions whether it is indeed ineffective for construction firms to develop their competitive advantage through innovation. By the application of statistical data across 18 Organization for Economic Cooperation and Development (OECD) countries and expert interviews in Singapore, innovation systems models are developed for both manufacturing and construction firms, respectively. Through comparison of both models, the results suggest that the peculiarities of the construction industry deem innovation as a poor competitive instrument for direct profits. Instead, construction firms can develop their competitive advantage through manipulating innovations that consumers are willing to pay for and innovations that would reduce construction costs. It is recommended that construction firms first utilize quality improvements to exploit consumers’ willingness to pay for innovative products. This initiative would enable construction firms to improve their finances for innovation and develop their “brand” in construction products. Sustainable competitive advantage could then be firmly established when construction firms engage in productivity improvements that lead to lower construction costs and/or faster completion times. This study concludes that innovation can be a useful competitive tool if construction firms aptly strategize it in according to its competitive environment.     

Productivity Analysis of Construction Operations

An interactive system for analysis of construction operations is proposed. The analysis is carried out in the context of various work modules which address quantity development, resource definition, and production and cost analysis. The quantity work module generates quantities based on information available in the design documents. The resource definition module receives and stores data regarding the labor∕equipment combination to be used to execute work tasks. This module provides the user with a set of standard useful construction process models. For each construction operation to be analyzed, the terminal describes the standard models. The user makes input of a set of parameters for process keyname, quantity, work task durations, number of resources, production capacity of each unit, and cost per hour of each unit to the standard model to be used. Using input from the resource definition module, the productivity and cost analysis module generates production rates and unit costs based on process simulation using CYCLONE methodology.     

工程项目管理中的成本管理

工程项目成本管理是为保障项目实际发生的成本不超过项目预算而开展的项目资源计划、项目成本估算、项目预算编制和项目预算控制等方面的管理活动。是保证施工企业正常经营的重要的基础管理工作。     

Comparative Analysis of Project Delivery Systems Cost Performance in Pacific Northwest Public Schools

Alternative project delivery systems such as construction management at risk (CMR) are increasingly used in public school construction in the United States. CMR is expected to benefit owners with a guaranteed maximum price (GMP), decreased change order cost, and increased cost “certainty.” This paper empirically compares cost growth performance of the CMR and design-bid-build (DBB) methods in Pacific Northwest public school projects. Data were collected from state records and previous studies on 297 completed schools in Oregon and Washington. The analysis of the data shows no statistically significant difference between CMR and DBB in construction change order costs, school project costs exceeding the GMP in 75% of the cases, and a statistically significant difference in cost growth between CMR and DBB projects during buy out, making CMR projects less efficient at controlling cost growth at buy out. These results counter some of the traditional expectations of the CMR delivery method.     

Cost-Effectiveness Evaluation of Warranty Pavement Projects

Warranty contracts are expected to enhance product quality and service life at lower life-cycle cost. Recognizing that such benefits could be offset by the generally higher costs of warranties, an assessment of the relative cost-effectiveness of warranty contracts compared to their traditional counterparts is currently of great interest. Using pavement data from Indiana, this paper evaluates the costs, effectiveness, and cost-effectiveness of warranty and traditional contracts. Effectiveness was measured in terms of pavement condition and treatment service life. The paper determined that the warranty contracts generally had higher agency costs but produced pavements with superior condition and service life, and lower construction periods and work-zone user costs, compared to their traditional counterparts. Over a 5-year period, the warranty contracts were found to be 27–30% less cost-effective than their traditional counterparts. Over the long term, the warranty contracts were found to be 70–90% more cost-effective on the basis of service life and 58–65% more cost-effective on the basis of both service life and pavement condition. The study results suggest that the higher long-term cost-effectiveness of warranty contracts is more perceptible when both cost and effectiveness are viewed over the entire life of the pavement treatment and when both agency and user cost are used in the cost analysis. The paper discusses certain aspects of such comparative evaluation studies that could lead to biased inferences and calls for careful screening of warranty and traditional contracts for such studies.     

Modeling Cost Escalation in Large Infrastructure Projects

Cost overruns in large infrastructure projects have been commonplace in the past decades. Budgeting for cost escalation is a major issue in the planning phase of these projects. In this paper, we first review various methods of forecasting escalation factor and study the changes in construction costs in the past 25 years by analyzing movements of a cost index. We then introduce a system for modeling the escalation uncertainty in large multiyear construction projects. The system uses a Monte Carlo simulation approach and considers variability of project component durations and the uncertainty of escalation factor during the project lifetime and calculates the distribution for the cost. System application is demonstrated using a numerical example. The system can be used by planners and cost estimators for budgeting the effect of cost escalation in large projects with multiyear schedules.     

Florida DOT Project-Level Bridge Management Models

The Florida Department of Transportation (FDOT) is in the process of implementing Pontis, a Bridge Management System, to provide decision support to engineers in the headquarters and district offices as they make routine policy, programming, and budgeting decisions regarding the preservation and improvement of the state’s bridges. As part of this effort, an ongoing research program is underway to adapt the system to FDOT needs as well as to advance the state of the art in several areas important to the Department. Most of the research results are organized around a new project-level decision support framework that complements and builds on Pontis’ existing network-level analysis. Specific new models include accident risk and user cost due to roadway width and alignment deficiencies; user cost of load capacity and vertical clearance restrictions, and moveable bridge openings; project-level prediction models for bridge element condition and costs; and prediction of economics of scale and scoping possibilities. The new models are built into a highly graphical spreadsheet model for decision support use.     

Cost Optimization Model for the Multiresource Leveling Problem with Allowed Activity Splitting

Resource leveling aims at minimizing the resource usage fluctuations, which is accomplished by moving noncritical activities within their float. The project duration is fixed and is not affected by the leveling. Most of resource leveling techniques assumes that activities cannot be split. Although this assumption is valid for most construction activities, there are several activities that can be split to achieve better resource leveling. However, there is an added cost associated with splitting such as startup and restarting costs. This paper presents an optimization model for resource leveling that allows activity splitting and minimizes its associated costs. The objective is to level resources in a way that provides a tradeoff between the extra cost of acquiring and releasing resources versus the extra cost of activity splitting. The model can be used to determine at what values of the splitting cost, the preemption of an activity is recommended. One example problem is solved at the end of the paper in order to illustrate the proposed model.     

Validation and Application of Empirical Liquefaction Models

Empirical liquefaction models (ELMs) are the standard approach for predicting the occurrence of soil liquefaction. These models are typically based on in situ index tests, such as the standard penetration test (SPT) and cone penetration test (CPT), and are broadly classified as deterministic and probabilistic models. No objective and quantitative comparison of these models have been published. Similarly, no rigorous procedure has been published for choosing the threshold required for probabilistic models. This paper provides (1) a quantitative comparison of the predictive performance of ELMs; (2) a reproducible method for choosing the threshold that is needed to apply the probabilistic ELMs; and (3) an alternative deterministic and probabilistic ELM based on the machine learning algorithm, known as support vector machine (SVM). Deterministic and probabilistic ELMs have been developed for SPT and CPT data. For deterministic ELMs, we compare the “simplified procedure,” the Bayesian updating method, and the SVM models for both SPT and CPT data. For probabilistic ELMs, we compare the Bayesian updating method with the SVM models. We compare these different approaches within a quantitative validation framework. This framework includes validation metrics developed within the statistics and artificial intelligence fields that are not common in the geotechnical literature. We incorporate estimated costs associated with risk as well as with risk mitigation. We conclude that (1) the best performing ELM depends on the associated costs; (2) the unique costs associated with an individual project directly determine the optimal threshold for the probabilistic ELMs; and (3) the more recent ELMs only marginally improve prediction accuracy; thus, efforts should focus on improving data collection.