Highway Construction Cost Research Articles

Prediction and Estimation of Highway Construction Cost using Machine Learning

Prediction and Estimation of Highway Construction Cost using Machine Learning

Preliminary Cost Estimation of Highway Projects Using Statistical Learning Methods

Setting up workable budgets symbolizes the competence of state highway agencies (SHAs) in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary estimates. Hence, based on the 1,249 projects retrieved from the Florida Department of Transportation (FDOT) database, this research aimed to develop a cost estimation model using statistical learning methods for SHAs to forecast preliminary costs during the early stages of a transportation project to fulfill different cost control and managerial functions. However, the currently used methods have serious limitations. This study introduced alternative statistical learning approaches to the currently most used methods: least absolute shrinkage and selection operator (LASSO) and general regression neural network (GRNN). LASSO regression, for instance, has proved in other areas of science to be remarkably better in terms of variable selection, interpretability, and numerical stability. In addition, this study also accounted for economic factors in model development because economic conditions are influential on highway construction costs but have received limited attention. Using the same dataset, LASSO and GRNN models were developed, and then their performances were evaluated based on a set of criteria, e.g., the mean absolute error and mean absolute percentage error. In comparison to the current practice with state DOTs, this research contributes to the body of knowledge by introducing a series of objective modeling approaches that can prevent human errors, requiring no substantial experience in preliminary estimating. Besides the introduction of statistical learning methods, this study took economic indicators into account when developing the models because they are important factors but have been ignored in previous studies. In addition, these statistical learning methods can produce reliable estimates in a much faster and more consistent fashion, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate.

Early Highway Construction Cost Estimation: Selection of Key Cost Drivers

Cost estimates in the early stages of project development are essential for making the right decisions, but they are a huge challenge and risk for owners and potential contractors due to limited information about the characteristics of a future highway project. Whereas previous studies were mainly focused on achieving the highest possible estimation accuracy, this paper aims to propose cost-estimation models that can provide satisfactory accuracy with the least possible effort and to compare the perspectives of owners and contractors as the key stakeholders on projects. To determine cost drivers (CDs) that have a high influence on highway-construction costs and require low effort for their establishment, a questionnaire survey was conducted. Based on the key stakeholders’ perceptions and collected data set, cost-estimation models were developed using multiple-regression analysis, artificial neural networks, and XGBoost. The results show that reasonable cost-estimation accuracy can be achieved with relatively low effort for three CDs for the owners’ perspective and five CDs for the contractors’ perspective. Additional inclusion of input CDs in models does not necessarily imply an increase in accuracy. Also, the questionnaire results show that owners are more concerned about environmental issues, whereas contractors are more concerned about the possible changes in resource prices (especially after recent high increases caused by COVID-19 and the Russia–Ukraine war). These findings can help owners and potential contractors in intelligent decision-making in the early stages of future highway-construction projects.

Conceptual cost estimation of highway bid items – A systematic literature review

PurposeChallenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.Design/methodology/approachThis paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.FindingsFindings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.Originality/valueThis paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

Modeling and Indexing the Cost of Highway Projects to a Responsive Highway User Fee

Multiple variables affect the costs of highway projects and associated bid item prices. Such variables constantly change according to factors on the project, state, and national levels. In most states, the dependency on a fixed fuel tax does not link the actual funding cost of highway projects to fees collected from highway users. To establish a link between the highway user fee and the actual costs of highway projects, this research aims to establish a methodology to identify highly significant and influential bid items, quantify the factors affecting the cost of highway projects, develop a bid item price estimation model, and calculate a highway construction cost index. The research identifies significant bid line items and selects “bituminous material for tack coat” to illustrate the research process. The research models the relationship between the bid item price and multiple variables using multiple linear regression and time series analysis. The Fisher price index is used to calculate a highway construction cost index. As a result, the research develops a modeling and indexing approach that can be used to link the actual cost of highway projects to a highway user fee.

Prevailing Wage Repeal, Highway Construction Costs, and Bid Competition in Kentucky: A Difference-in-Differences and Fixed Effects Analysis

Data for over 2,000 highway pavement projects, constructed between 2014 and 2020, are used to examine the effect of Kentucky’s prevailing wage repeal in 2017 on relative bid costs and bid competition for state and federally funded work. Other than Davis-Bacon and Disadvantaged Business Enterprise policies for federal projects, and prevailing wage requirements on state-funded work prior to 2017, all highway construction in Kentucky is built to the same standard. A difference-in-differences and fixed effects analysis fails to find statistically significant differences in bid costs and competition between federal and state projects before and after repeal. Competition on pavement projects in Kentucky is very low compared to other states. Consequently, the impact of increased competition on bid costs is very high. Policies increasing competition in Kentucky would be more effective in reducing costs than prevailing wage repeal.

Evaluating the impact of uncertainty events on the cost of linear infrastructure projects

This research examined the uncertainty events encountered in the process of constructing highways and evaluates their impact on South African highway construction costs. Due to the lack of appropriate evaluation of the impact of uncertainty events encountered in the construction process of highway projects, the construction costs of these projects are underestimated. To counteract such underestimation, this research developed an adaptive neuro-fuzzy inference system (Anfis) to assess the impact of uncertainty events involved in the construction of linear infrastructure projects. To validate the Anfis model, the stepwise regression analysis (SRA) and fuzzy Bayesian network (FBN) models were designed, and their results were compared with the outputs of Anfis. The prediction performance comparison proved that Anfis has a higher performance than SRA and FBN. Based on the results of the study, it can be deduced that the Anfis model is an accurate and reliable technique in assessing the impact of uncertainty events on the cost of construction projects. Therefore, the study concluded that using hybrid intelligent machine learning techniques not only minimises the time and difficulty of the estimation process but also reduces the potential inconsistency of correlation between variables in construction cost prediction.

The Effect of “Federal -Aid Swap” Programs and Davis-Bacon Prevailing Wages on Highway Construction Costs and Contractor Composition: Evidence From Iowa

“Federal-aid swap” programs allow states and local governments to bypass federal Davis-Bacon prevailing wages and Disadvantaged Business Enterprise (DBE) goals by exchanging federal funds that have been allocated to highway projects with state funds. The Iowa Department of Transportation approved a federal-aid swap program in February 2018. Using data on more than 1,200 highway construction projects in Iowa from 2016 to 2020, I find that the cost of projects in the federal-aid swap program are not statistically different from those that were not swapped, after accounting for project size and complexity, project type, and project location. Regression results indicate that Davis-Bacon prevailing wages and DBE goals have no effect on total construction costs. However, the federal-aid swap program is statistically associated with a decrease in the likelihoods that a project is covered by the Davis-Bacon Act by 10 percentage points and DBE goals by 4 percentage points. Because the payment of Davis-Bacon prevailing wages is statistically associated with an 8 percentage-point decrease in the chances that a highway project is awarded to an out-of-state contractor, the federal-aid swap program may have increased the market share of out-of-state contractors at the expense of Iowa-based contractors.

Holistic Framework for Highway Construction Cost Index Development Based on Inconsistent Pay Items

AbstractA construction cost index (CCI) measures the price changes of construction items over time. It allows owner agencies and contractors to monitor construction market fluctuations so that they...

Data-Driven Predictive Modeling of Highway Construction Cost Items

AbstractThe highway network is an economically necessary form of transportation that has a significant impact on the quality of the life of the citizens who use it. Cost overruns in highway project...

Geo-Characterisation of Highway Construction Cost Drivers in the Niger Delta Region of Nigeria

Geo-Characterisation of Highway Construction Cost Drivers in the Niger Delta Region of Nigeria

Predicting the Volatility of Highway Construction Cost Index Using Long Short-Term Memory

The highway construction cost index (HCCI) is a composite indicator that reflects the price trend of the highway construction industry. Most available indexes exhibit significant variation, creating a challenge for state agencies to make accurate budget estimations. Numerous researchers have attempted to forecast the index using quantitative models, but two major problems still exist. First, few models work effectively with highly volatile data. A model that is only fitted well with stable data does not validate its forecasting power. Second, a good prediction model should be able to forecast at different time horizons. Many prior research projects only predicted one index point ahead, limiting the application effectivity in practice. This research fills the gap by applying the long short-term memory (LSTM) units built in the encoder and decoder architecture to model and predict the variation of the HCCI. An illustrative example used the Texas HCCI as the raw data, and the results were compared to the seasonal autoregressive integrated moving average model. The results show that the developed LSTM model outperformed the time series models in terms of providing more accurate prediction in all three forecasting scenarios, short-term, medium-term, and long-term prediction. The main contributions of this study to the body of knowledge in cost engineering and forecasting are summarized in the following two areas: first, the paper presents a novel application of an artificial intelligence algorithm for cost index forecasting that provides more accurate prediction than the prevailing time series models, particularly for highly volatile cost indexes. Future researchers could be benefited from the explored results in this paper and use them as a referenced benchmark. Second, this is one of the first papers in construction management that shows the performance of the forecasting models when shape-change of index exists.

Profile Monitoring for Examining Impact of Hurricane Katrina and Rita on Highway Construction Cost

Extensive destruction and damage to communities in the aftermath of large-scale disasters such as hurricanes, floods, and earthquakes can cause substantial disorder in social and economic activity. The ensuing repercussions to social and economic systems lead to considerable uncertainty in continuing existing construction projects and implementing new reconstruction is problematic for accurately determining construction costs and making appropriate investment decisions. A large-scale disaster magnifies the importance of empirically examining variations in construction costs. Therefore, the overarching objective of this research is to analyze submitted unit price bids for superpave asphaltic concrete line items for highway projects let in the state of Louisiana after hurricanes Katrina and Rita. We employ two-way analysis of variance and a cumulative sum (CUSUM) control chart to monitor and detect changes in submitted unit price bids for hurricane- and non-hurricane-impacted areas. The results of the CUSUM control chart indicate that unit price bids for asphalt line items in the impacted areas significantly increased immediately after Hurricane Katrina and Rita hit the Gulf of Mexico, and those in the non-hurricane-impacted areas significantly increased six months after the hurricane. The findings of this research enable transportation agencies to more closely estimate the cost of rebuilding highway projects and make more-informed investment decisions for highway recovery programs in the aftermath of large-scale disasters.

Impacts of pavement deterioration and maintenance cost on Pareto-efficient contracts for highway franchising

In a build-operate-transfer (BOT) highway project, pavement roughness progression generally has a direct impact on the highway maintenance cost and user costs. Given two common rehabilitation strategies, this study respectively investigates the optimally designed highway capacity and tolls of multi-type vehicles in Pareto-efficient contracts. Under supplementary conditions, it proves that the optimal toll revenue could fully cover the highway construction and maintenance cost in these contracts, no matter with or without the regulated traffic composition. Further extensions are made to evaluate and identify properties of the highway v/c ratio, rehabilitation period or critical roughness under the two rehabilitation strategies, respectively.

Analysis for Dowel Bar’s Set Interval of Cement Concrete Pavement

Through comprehensively considering transverse joint load transfer, dowel bar shearing, dowel bar bending, concrete’s borne dowel bar pressure and impact on pavement slab bottom’s flexural-tensile stress, adopting R · D · Bradbury dowel bar practical design checking formula and cement concrete pavement three-dimensional finite element analysis program EverFE for discussion of dowel bar’s set interval and combining with testing observation, I view that, dowel bar interval can be appropriately increased when pavement slab thickness is greater than 24cm which has practical significance for saving consumption for reinforcing steel bars of dowel bar and reasonably controlling highway construction cost.

Forecasting Completed Cost of Highway Construction Projects Using LASSO Regularized Regression

Finishing highway projects within budget is critical for state highway agencies (SHAs) because budget overruns can result in severe damage to their reputation and credibility. Cost overruns in highway projects have plagued public agencies globally. Hence, this research aims to develop a parametric cost estimation model for SHAs to forecast the completed project cost prior to project execution to take necessary measures to prevent cost escalation. Ordinary least square (OLS) regression has been a commonly used parametric estimation method in the literature. However, OLS regression has certain limitations. It, for instance, requires strict statistical assumptions. This paper proposes an alternative approach—least absolute shrinkage and selection operator (LASSO)—that has proved in other fields of research to be significantly better than the OLS method in many respects, including automatic feature selection, the ability to handle highly correlated data, ease of interpretability, and numerical stability of the model predictions. Another contribution to the body of knowledge is that this study simultaneously explores project-related variables with some economic factors that have not been used in previous research, but economic conditions are widely considered to be influential on highway construction costs. The data were separated into two groups: one for training the model and the other for validation purposes. Using the same dataset, both LASSO and OLS were used to build models, and then their performance was evaluated based on the mean absolute error, mean absolute percentage error, and root mean square error. The results showed that the LASSO regression model outperformed the OLS regression model based on the criteria.

SPATIAL REGRESSION ANALYSIS FOR MODELING THE SPATIAL VARIATION IN HIGHWAY CONSTRUCTION COSTS

Price volatility in wages, materials, and equipment has a significant impact on highway construction costs. As the construction market and economy have experienced dynamic changes in prices, the price volatility becomes less predictable. In addition, various levels of the price volatility in different market locations aggravate the prediction. Thus, in developing highway construction costs, transportation agencies should consider geographical location of construction projects and market conditions of the locations. Transportation agencies face significant uncertainties in price volatility across different geographical locations. This volatility may not be uniformly distributed across different geographical locations due to changes in the availability of local contractors, materials, equipment, and labor. The objective of this research is to develop statistical models that are capable to explain spatial variations in submitted unit prices for asphalt line items in highway projects considering local market condition factors. Historical bid data used in this research consist of resurfacing and widening projects let in the state of Georgia, the United States, between 2008 and 2015. The methodology of this research is a spatial regression analysis to explain the spatial variation in the submitted unit prices for asphalt line items. The findings of this research indicate that volatility in submitted bid prices is not uniformly distributed across different geographical locations within the same transportation agency. The contribution to the body of knowledge of this research is an improved understanding of the role of local construction market and macroeconomic conditions to explain geographic variability in construction costs.

Multidimensional Highway Construction Cost Indexes Using Dynamic Item Basket

AbstractA highway construction cost index (HCCI) is an indicator of the purchasing power of a highway agency. Thus, it must reflect the actual construction market conditions. However, current metho...

An Economic Calibration Method for Fuel Consumption Model in HDM4

HDM4 developed by Worldbank is the calculation software which is widely used in estimating various costs such as highway construction cost and vehicle operating cost (VOC). The necessary data such as weather condition, traffic volume, running speed, road curvature and road gradient should be input to calculate fuel consumption in HDM4. In conventional HDM4 calibration, calibration factors are determined by the average fuel consumption which has been measured for various vehicles for long period. The vehicles which are released after the calibration factors are determined or those with new technology could be low in terms of fuel consumption. This paper has proposed a fuel consumption testing method for quick and easy determination of the calibration factors in HDM4 VOC calculation. The calibration for the fuel consumption of HDM4 is performed by measuring fuel consumption for round trips on two flat and straight roads at several steady speeds for a short period. By this calibration method the factors for driving resistance ranged from 0.13 to 0.30. Latest cars are lower than HDM4 models in terms of tractive resistance and power loss with more efficient fuel economy. Therefore, it appears that the calibration factors would further decrease over time. In case of HDM4, however, separate calibration factors are not provided for aerodynamic resistance. Therefore, it is reasonable to regard that the difference in aerodynamic resistance has been reflected to the two calibration factors.

Optimal Trade-Offs between Construction Cost and Traffic Delay for Highway Work Zones

AbstractHighway work zones often cause traffic congestion, resulting in increased road-user delays and vehicle emissions. These negative impacts of highway work zones can be minimized by a number of mitigating measures such as reducing the length of work zone segments, using the shoulder temporarily for traffic, and working during low-traffic nighttime hours. These work zone layout measures, however, cause an increase in highway construction cost. Accordingly, these tradeoffs between reducing work zone delays and minimizing construction cost need to be analyzed to establish an optimal balance between these two critical and conflicting objectives. This paper presents the development of a novel multiobjective optimization model for highway work zone layouts that is capable of generating optimal tradeoffs between minimizing traffic delays and construction cost. The model was developed in three main phases: formulation, implementation, and performance evaluation. First, the model was formulated to (1) identif...