Highway construction costs are subject to significant variations over time. These fluctuations are apparent in trends of indices, such as the national highway construction cost index (NHCCI). These variations are problematic for highway contractors because they can result in bid loss or profit loss. They are also problematic for owner organizations, such as State Departments of Transportation (State DOTs), because they can result in hidden price contingencies, delayed or cancelled projects, inconsistency in budgets, and unsteady flow of projects. These problems can be prevented if highway construction costs are forecasted accurately. Existing literature lacks appropriate models to accurately forecast NHCCI. The objective of this research is to identify the leading indicators of NHCCI (i.e., the explanatory variables of NHCCI that are useful to predict NHCCI), and create appropriate multivariate time series models for forecasting NHCCI through utilizing information available from the identified lea...

Because of the importance of budget planning and contract bidding, accurate prediction of future movements of the construction cost index (CCI) has been a crucial part of construction cost management. Despite the fact that construction costs can vary widely across locations with different market conditions and environments, the national CCI, the simple average of construction costs for 20 US metropolitan areas, has often been used to forecast CCIs across the nation. This study finds considerable differences across US cities in both the level and growth rates of CCIs and shows that using the national CCI for predicting city-level CCIs can cause nonnegligible forecast errors. Comparing four popular linear forecasting models, including standard autoregressive integrated moving average (ARIMA) models and a multivariate vector error correction model (VECM) drawn on monthly CCI data from January 1995 to December 2019, this study reveals that no single model is dominant in terms of the out-of-sample performance. If any leading indicator is available at the city level, however, it is recommended to choose between the ARIMA model based on the Bayesian information criterion (BIC) rule and the multivariate VECM augmented with the leading indicators. For the leading indicator in multivariate VECM, city consumer price index (CPI) works better than national CPI. In the absence of such leading indicators, it is recommended to use a parsimonious ARIMA model based on the BIC rule. The proposed approach is expected to help decision makers in the construction industry prepare more accurate budgets and biddings for regional construction projects.

The criticality of seismic robustness of the water pipe networks cannot be overstated. Current methodologies for optimizing seismic robustness of city-scale water pipe networks are scarce. A very few studies that can be found are also prone to long optimization runtimes due to the requirement of repeated hydraulic analysis. Hence, there is a critical need for the identification of computationally efficient surrogate optimization methods for maximizing seismic robustness of water pipe networks. To address this need, this research was conducted to identify, for the first time, computationally efficient topological surrogates for hydraulic simulation-based optimization. The computational efficiency of surrogate optimization was measured in terms of solution quality (i.e., post-earthquake serviceability) and computational runtime. Ten different topological connectivity metrics were evaluated out of which five were considered computationally infeasible due to their prohibitive optimization runtime. Five remaining metrics were then used to formulate five surrogate objective functions for seismic robustness of water pipe networks. Each of these functions was optimized using a simulated annealing-based algorithm. Application of the proposed approach to city-level benchmark networks helped to identify two metrics out of ten that offered a substantial reduction in optimization runtime with a minimal loss in solution quality. These findings will be highly valuable to water distribution network managers for identifying economical rehabilitation policies for enhancing the seismic robustness at a city-scale within a reasonable amount of time.

Utility managers in charge of water pipe networks that are exposed to high seismicity make difficult decisions regarding the allocation of a limited rehabilitation budget to be most effective in enhancing a network’s postearthquake serviceability. Seismic vulnerability models are typically integrated with simple prioritization methods to identify the critical pipes subjected to earthquakes. These methods do not distribute resources at the system level and may not provide an economical solution. The objective of this paper is to develop an approach to identify the critical pipes for proactive seismic rehabilitation that will enhance a network’s postearthquake serviceability when only a finite length of pipes can be rehabilitated. To achieve this objective, a proper stochastic combinatorial optimization was formulated and then solved, using a genetic algorithm that was integrated with a network-level seismic vulnerability model. The approach was implemented to identify critical links for proactive seismic rehabilitation of two benchmark networks. The results showed that this approach outperforms the simple length-based prioritization methods used by the utilities, as well as the latest proposed methodology in the literature, in identifying the critical pipes in a water pipe network subjected to an earthquake.

Underground freight transportation (UFT) is a class of automated transportation systems in which vehicles carry freight through tunnels or pipelines under roadways between intermodal terminals. The UFT systems increase the current freight movement capacity and alleviate the problems associated with the fast growing need for freight transportation. Despite the inherent UFT benefits to alleviate traffic congestion and enhance safety and security of freight transportation while improving sustainability, a detailed lifecycle benefit-cost analysis is yet required to explicitly show planners and decision makers the economic feasibility of such systems. The primary objective of this paper is to conduct a comprehensive lifecycle benefit-cost analysis of linear induction motor-based UFT systems for five different scenarios. This methodology includes the following steps: (1) developing UFT scenarios with different sizes (small, medium, and large) and routes (short and long) in collaboration with a stakeholder committee; (2) determining lifecycle cash flows; (3) calculating net present values, benefit-cost ratios, and internal rates of returns; (4) conducting sensitivity analysis; and (5) performing breakeven analysis. The results of this research show that the highest benefits of the UFT systems are revenue from shipment and reduction in air pollution. The internal rates of return, benefit-cost ratios, and net present values of the UFT scenarios indicate that the benefits offset the costs of the system. Results of sensitivity analysis show that the economic feasibility of the large and medium-size UFTs is more sensitive to tunnel construction cost and revenue from shipments. They also indicate that the economic feasibility of the UFT system with a short route and small size is more sensitive to higher discount rates and the policies concerning the shipment pricing. Results of breakeven analysis highlight a shorter payback period for the large and medium-size UFTs compared with small-size scenarios. The results show that the price of shipment for all the UFT scenarios is highly competitive compared with the current price of shipment by trucks.

Construction spending (CS) represents the level of construction activities in the United States. It has been widely used for evaluating and tracking the construction market. Evaluation of the construction market provides valuable information for construction firms to plan resources and make strategic business decisions. Architecture Billings Index (ABI) has been introduced as a potential leading indicator of construction spending. Correlation analysis and regression modeling have been used in the literature to conclude that ABI leads nonresidential construction spending up to 1 year. This paper argues that these methods are not the right methods for studying the temporal relationship between these two time series because they ignore autocorrelation among the values of each time series. In other words, the autocorrelation may result in spurious correlation between variables simply by coincidence. This is further confirmed in this paper showing that neither ABI nor construction spending are stationary, and therefore, correlation analysis and regression modeling could lead to wrong interpretations. The research objective is to empirically analyze the temporal association between ABI and construction spending using time-series methods. Unlike the findings indicated in the literature, the results of this research show that institutional ABI leads nonresidential construction spending only in higher lag lengths (12 months and longer). There was no evidence that commercial/industrial ABI leads nonresidential construction spending. The results also show that residential ABI leads residential construction spending up to approximately 3 years. Moreover, it is found that institutional and residential ABIs have long-term temporal relationships with nonresidential and residential construction spending variables, respectively. These results are validated through the creation of appropriate time-series models that are capable of identifying future trends in construction spending. The findings of this paper contribute to the state of knowledge by identifying and quantifying the temporal relationships between ABI and construction spending. It is expected that these findings will help construction firms identify future trends in construction activities. The identified trend in construction spending provides useful information to construction firms in making better strategic business decisions in the volatile construction market.