Contrary to the popular belief cited in the literature, the proposed data analytics technique shows that multiple linear regression (MLR) can achieve as high a predictive power as some of the black box models when the necessary interventions are implemented pertaining to the regression diagnostic. Such an MLR model can be utilised to design an optimal concrete mix, as it provides the explicit and accurate relationships between the HPC components and the expected compressive strength. Moreover, the proposed study offers a decision support tool incorporating the Extreme Gradient Boosting (XGB) model to bridge the gap between black-box models and practitioners. The tool can be used to make faster, more data-driven, and accurate managerial decisions without having any expertise in the required fields, which would reduce a substantial amount of time, cost, and effort spent on measurement procedures of the compressive strength of HPC.

The Moving Ahead for Progress in the 21st Century Act (MAP-21), enacted by the US Congress in 2012, establishes seven national infrastructure performance goals that must be considered during funding allocation procedures by state transportation agencies (STAs). These goals’ wide range of aspects creates situations where STAs have to deal with conflicting objectives without formal trade-off mechanisms, forcing them to make investments that might be difficult to justify to stakeholders. Moreover, STAs strive to create standard performance metrics that allow the comparison of investment alternatives among different groups of assets. This paper presents a case study on applying a Multi-Attribute Utility Theory (MAUT) model to make trade-offs among conflicting objectives in different types of transportation construction projects. The case study was conducted on ten construction projects executed by the Iowa Department of Transportation. The case study shows how the proposed MAUT model can be used as an objective mechanism to prioritize infrastructure projects across asset classes to provide the necessary justification for infrastructure policy decision-making.

The appropriate selection of procurement tools and strategies is a key factor in the successful completion of construction projects. Despite the increasing use of alternative project delivery methods by the public agencies, the traditional design–bid–build (DBB) approach is still the most used and accepted project delivery method in the US. The purpose of this study is to present an innovative tool that introduces an objective criterion into the selection of resurfacing projects for design–bid–build —lump sum (DBB-LS) projects. This criterion consists of a comparison of the stochastic construction cost estimate for a candidate project if procured under DBB-LS versus its stochastic cost estimate if a unit price (UP) approach is used. The proposed decision-making tool was developed using non-linear regression techniques, Monte Carlo Simulation, and data from 86 resurfacing projects completed by the Florida Department of Transportation (FDOT) between January 2015 and March 2017: 63 UP and 23 LS projects. To facilitate the use of the tool by decision makers, the mathematical functions involved in the cost comparison between these two approaches have been comprised into a nomogram, allowing a quick approximation of the probability of having a lower cost under each compensation approach and the potential savings or extra costs of using an LS provision. The nomogram also allows the estimation of potential LS cost implications under different confidence levels, providing FDOT with the ability to make decisions at different levels of risk.

Transportation agencies provide guidance on using bid data from previous projects to prepare construction cost estimates that are based on assumptions whose validity is not challenged in the existing literature. Some of these assumptions refer to the optimal number of years of past data that should be used in bid-based estimating. This study aims to develop an approach that assists with the definition of optimal look-back periods for data retrieval to maximize cost estimating effectiveness in transportation construction projects. Identifying the optimal amount of data is irrelevant if it is not properly collected and processed. The proposed look-back period determination process is presented as part of a broader cost estimating system intended to ensure the effective use of the retrieved data. The paper presents an assessment of 20 cost indexing alternatives to identify the one that facilitates the highest estimating accuracy. The methodology consists of an innovative moving-window cross-validation algorithm designed to measure the performance of the estimating system for different look-back periods ranging from one to five years.