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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. The conventionally used cost-based approach, adopted by most departments of transportation (DOTs) in practice, is time consuming and deeply reliant on estimators' experience and sound judgments to make dependable estimates. However, subjective judgments of estimators are often inconsistent and unreliable, leading to erratic and erroneous estimates. The increasingly worsening labor shortage compounds the issue because the accuracy of preliminary cost estimates largely depends on estimators' experience in current practice, requiring a long time to obtain. Therefore, the advantages of this research include the circumvention of human errors because they require no substantial experience in estimating. In addition, these proposed statistical learning methods produce reliable estimates in a much faster and more consistent fashion than the conventional estimating methods, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate. Based on the 1,249 projects retrieved from the 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. [ABSTRACT FROM AUTHOR]

The paper is concerned with the development and comparison of alternative machine learning methods of determining the type of truck crossing a bridge from the dynamic response it induces within the bridge structure, the so-called weigh-in-motion problem. Weigh-in-motion is a rich engineering problem presenting many challenges for current machine learning technologies, and for this reason is proposed as a benchmark for guiding and assessing advances in the application of this field of artificial intelligence. A review is first provided of existing methods of determining truck types and loading attributes using both machine learning and heuristic search techniques. The most promising approach to date, that of artificial neural networks, is then compared to support vector machines in a comprehensive study considering a range of configurations of both modeling techniques. A local scatter point smoothing schema is adopted as a means of selecting an optimal set of design parameters for each model type. Three main model formats are considered: (i) a monolithic model structure with a one-versus-all truck type classification strategy; (ii) an array of sub-models each dedicated to one truck type with a one-versus-all classification strategy; and (iii) an array of sub-models each dedicated to selecting between pairs of trucks in a one-versus-one classification strategy. Overall, the formats that used an array of sub-models performed best at truck classification, with the support vector machines having a slight edge over the artificial neural networks. The paper concludes with some suggestions for extending the work to a broader scope of problems. [ABSTRACT FROM AUTHOR]

This study has investigated the mitigations necessary for successful compatibility between International Mobile Telecommunications (IMT) services and Fixed Satellite Service (FSS) Earth Stations operating in adjacent frequency bands. We consider a problem where IMT services operate in the 3.4–3.6 GHz frequency band and FSS Earth Stations operate in the adjacent 3.6–3.8 GHz band (both bands are within the frequency range designated C-band). The results presented in this paper are applicable to other boundaries between IMT and FSS operating in C-band. This is an adjacent band compatibility problem, hence the mitigation considered is based on frequency separation. We firstly perform a co-frequency interference analysis and then determine the Net Filter Discrimination available for a range of possible frequency separations including Guard Bands. These calculations rest on some assumptions regarding the IMT transmitter and FSS receiver spectrum masks. We present some results which show that an 18 MHz Guard Band is sufficient to mitigate interference. This analysis is based on the median aggregate interference-to-noise ratio Σ I / N delivered by our co-frequency analysis, using a − 10 dB threshold and covering all of the spectrum mask combinations considered in the study. [ABSTRACT FROM AUTHOR]

While unmanned aerial vehicles (UAVs) are extensively used for data collection in the construction industry, currently, no comprehensive quantitative method measures the indirect risks workers face from flying UAVs on jobsites. Augmenting past work addressing the direct risks of UAV flights, namely, crashes and collisions, this paper assesses the indirect UAV flight risks triggered by the proximity of UAVs with humans. Risk assessment is simple in theory: any risk may be quantified by knowing the probability of a risky event, the probability of impact, and/or the severity of the event. This paper (1) discusses the nature of the probability of a risky event, the probability of impact, and the probability of severity of UAV flight indirect risks and (2) presents a model for measuring the indirect risks of UAV flights when associated with the proximity of construction workers and UAVs. The proposed model is applied to a case study using Monte Carlo Simulation, and the results show the applicability of the model to a construction jobsite environment. [ABSTRACT FROM AUTHOR]

This article is focused on developing an Energy Service Company (ESCO) risk assessment model for use by energy savings certificates (ESC) market regulators. This model enables market regulators to determine the appropriate point in time for ESCOs to sell their certificates with the aim of minimizing risk as well as maximizing economic gain yet remain motivated for reducing the cost of energy efficiency technologies. To this end, the interactions between an ESCO and other parties (such as suppliers) in the market in addition to the principles of the energy efficiency performance contract are taken into consideration. Then, appropriate probability distributions have been fitted to the stochastic variables to be applied in the Net Present Value (NPV) function, based on sampled company data. A case study considers a one MW Organic Rankine Cycle (ORC) implementation in Iran's petrochemical industry. The finding of this study shows if the ESCO is allowed to sell the certificates during the first seven years as well reduce 30% of the investment cost, the expected Net Present Value over Investment Cost (NPV/I) savings will cover more than one cycle. [ABSTRACT FROM AUTHOR]

Background: Construction companies typically work on many projects simultaneously each with its own objectives and resource demands. Consequently, a key managerial function is to allocate financial, equipment, and human resources between these concurrent projects in a way that satisfies the individual project constraints while optimizing the company's overall objectives. Project portfolio management is concerned with managing multiple projects to accomplish strategic goals. The main concentrations of research so far have been project selection, prioritization and alignment of a portfolio with strategic goals among a pool of awarded projects. The objective of this research is to develop a model capable of generating representative future streams of projects that can be used to assist in strategic planning and portfolio management. Methods: This paper reports on an on-going research project aimed at developing and validating a stochastic model of streams of uncertain and unknown future projects and to demonstrate the significance and implications of such uncertainties on project portfolio and strategic planning. The scope of the research is Florida Department of Transportation (FDOT) projects. Records of the past 12 years letting information combined with a pool of candidate variables is analyzed to capture characteristics of the time series data and to determine any project characteristic correlations with macroeconomic factors. Results: The performances of the various model components presented indicate the viability of an integrated project stream forecaster that predicts, within a simulation environment, the frequencies of projects and empirical distributions of project duration and cost. Conclusion: The gap in the current body of knowledge is a lack of consideration of the effects of uncertainties associated with future projects, both known (but yet to be awarded to a contractor) and unknown (although statistically quantifiable). It is evident that companies should not just focus on current known projects but also uncertain and unknown future projects. Such a capability, looking into the future, is key to the effective medium and long-term strategic planning for the company. Contractors can, for example, use these stochastic streams of data to test different bidding strategies and to see how sensitive the performance of their portfolio is to changes in different market factors. [ABSTRACT FROM AUTHOR]

Background: In the construction industry, the productivity of all trades is directly impacted by uncertainty and variability. For repetitive projects, smooth work flow of productive resources is necessary to minimize or eliminate interruptions and idle time with the objective of reducing costs. An ideal or near optimal solution requires careful planning of the sequence, timing and resource allocations for each activity. Earlier research has demonstrated that uncertainty in the duration of repeated activities can have a significant impact on what is determined to be the optimum project plan. This suggests that correlation in the duration of repeated activities (where durations are stochastic) may also be important in determining the most favorable plan. Methods: This study assesses the significance of correlation in this respect, using a Linear Scheduling framework for modeling repetitive construction work. Numerical and graphical results are used in a case study to evaluate and compare the optimal solution derived for both deterministic and stochastic environments. For the stochastic environment, a range of levels of correlation are considered using linear correlation between immediate successor repetitions of an activity. Results: The results provide insight into the effects of different degrees of correlation on the expected project duration, cost, crew and equipment idle times and interruptions. The correlation level has effects that translate into performance tradeoffs depending on the initial plan assumptions applied to the activities. Conclusions: The impact of full correlation on the optimality of a project plan was found, on average, to introduce idle time to crews equal to 7% of their active time, and to cause avoidable delays to the completion of a project equal to 12% of the project duration. The authors believe these inefficiencies justify further investigation of the impact of correlation on construction projects, including the development of more sophisticated models of correlation. [ABSTRACT FROM AUTHOR]

Graduate admission committees in some construction management graduate educational programs in the United States may sometimes require students to take and submit their GRE scores as part of the admission requirements. This is because they tend to believe that the GRE scores could predict the academic performance and success of students in graduate school. This study assessed the predictive ability of admission GRE scores on cumulative GPA scores at graduation of construction management graduate students. The hypothesis was that students who scored high in GRE at the time of admission would attain higher cumulative GPAs at graduation. The independent variables were GRE verbal score, GRE quantitative score, and total GRE score while the dependent variable was the GPA score. SPSS v21 and SAS v9.3 statistical analysis tools facilitated the analysis of the data sourced from three construction management graduate programs in the United States. The results showed weak predictive indices where GRE quantitative score was the better predictor of graduation GPA. Thus, the hypothesis of higher GPAs being associated with higher GRE scores was weakly supported. It was recommended that construction management graduate programs should re-evaluate requiring GRE scores as part of their admission criteria. [ABSTRACT FROM AUTHOR]

Historically, actual construction costs have tended to exceed initial cost estimates and budgets. Often this discrepancy is significant enough to cause problems such as depletions of budgets, disputes, and reductions in work quality. Cost contingency is an important element included in the base cost estimate to protect construction participants including owners, contractors, and architects from the risks associated with underestimating project cost estimates and overrunning cost budgets. Typically, project participants have simply calculated contingency as a fixed percentage of project cost in spite of the importance of contingency. The uniform application of this deterministic method to calculate contingency on the basis of project costs only is not appropriate for all construction projects. This paper identifies factors that influence contingency and proposes a new method for predicting the owner's financial contingency on transportation construction projects using an artificial neural network (ANN)-based method. Asphalt resurfacing works among transportation projects sponsored by the Florida Department of Transportation (FDOT) completed from 2004-2006 are used for this study. The results show the viability of the ANN approach in the prediction of contingency. Accurate predictions of contingencies using this approach can help project administrators better manage contingency requirements on financing projects, allowing a more optimal usage of available project funds. [ABSTRACT FROM AUTHOR]

The paper provides a review of empirical modeling and its application within construction engineering and management. The scope of application and trends in use of this approach are first assessed, and the potential for its further development is identified. This is followed by an examination of the key components of empirical modeling, namely: the structure and operation of the model and the scheme used in its development. The paper then provides a rigorous methodology that must be followed to ensure the validity and value of the end model, covering the steps: strategizing; data collation and assessment; model development; model evaluation and final selection; final validation; and implementation. The methodology is designed to cater for all forms of empirical modeling including the procedurally more demanding development algorithms that have become available in recent years, such as simulated evolution. Overall, the paper is designed to provide researchers embarking on an empirical modeling study with an overview of when it is appropriate to use this approach, what type of system to adopt, and how to ensure development of a successful end product. [ABSTRACT FROM AUTHOR]

The note reports on recent developments to the coarse-grain method (CGM) of modeling transient heat flow in buildings. CGM was originally developed as an alternative to conventional fine-grain modeling techniques [such as the finite-difference method (FDM) and finite-element method (FEM)] to increase simulation speed to a degree that facilitates three-dimensional modeling, and to ease the tasks of model development and experimentation. Earlier work has shown that CGM can provide reasonably accurate simulations at a processing speed several orders of magnitude faster than FDM or FEM. This note describes and demonstrates refinements to the CGM approach that increase its modeling accuracy to a level comparable to FEM, while doubling its processing speed. These refinements are: (1) the use of a hybrid linear regression model with an artificial neural network (ANN) to represent each coarse-grain modeling element (the hybridization of the ANN effectively halves its complexity); and (2) a linear calibration of the ANN-based coarse-grain modeling elements to account for an observed positive bias in their predictions. The improved approach is demonstrated for a two-dimensional model of a bay in a research building located at the University of Florida. The note concludes with some suggestions for continuing research. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a new approach for simulating the thermal behavior of buildings that overcome the limitations of conventional heat-transfer simulation methods such as the finite difference method and the finite element method. The proposed technique uses a coarse-grain approach to model development whereby each element represents a complete building component such as a wall, internal space, or floor. The thermal behavior of each coarse-grain element is captured using empirical modeling techniques such as artificial neural networks (ANNs). The main advantages of the approach compared to conventional simulation methods are (1) simplified model construction for the end-user; (2) simplified model reconfiguration; (3) significantly faster simulation runs (orders of magnitude faster for two- and three-dimensional models); and (4) potentially more accurate results. The paper demonstrates the viability of the approach through a number of experiments with a model of a composite wall. The approach is shown to be able to sustain highly accurate long-term simulation runs, if the coarse-grain modeling elements are implemented as ANNs. In contrast, an implementation of the coarse-grain elements using a linear model is shown to function inaccurately and erratically. The paper concludes with an identification of on-going work and future areas for development of the technique. [ABSTRACT FROM AUTHOR]

Proposes an artificial neural network based method of modeling the dynamic behavior of continua. Network development; Examination on the effectiveness of the proposed method; Conclusions.

Describes the Constructability Lessons Learned Database prototype, an edge method of automatically gathering, systematically organizing and efficiently applying vital construction information to a contractor's daily activities. Attempts at database constructability programs; Usage of constructability feedback system.

The article discusses the success stories of artificial neural networks in civil engineering. An analysis concludes that progress in applied artificial neural networks stagnated following the initial applications of the early 1990s. The five most frequently cited articles in the "Journal of Computing in Civil Engineering" are listed. Artificial neural networks have been a primary focus of interest for research in computer-based civil engineering.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods. [ABSTRACT FROM AUTHOR]