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.

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.

For more than a decade, unmanned aerial vehicles (UAVs) have been used on construction job sites for a variety of purposes including structural inspection, and 3D mapping of the site and existing structures. Although the advantages that UAVs have brought to construction are discussed extensively in the literature, applications of other types of unmanned vehicles (UVs) in the construction industry, including unmanned ground vehicles (UGVs), unmanned surface vehicles (USVs) (operating on water surfaces), and unmanned underwater vehicles (UUVs), have received limited attention. Although UAVs are the most frequently employed type of UVs in the construction industry, UGVs and USVs have also been involved in a few construction-related missions. These include search operations and structural damage assessment in the aftermath of disasters such as the World Trade Center in 2001, Fukushima Daiichi nuclear power plant in 2011, Hurricane Wilma in 2005 and Hurricane Ike in 2008. UGVs and USVs have rarely been used in any other type of construction-related application. Furthermore, there is no available data about construction-related applications of UUVs. Recent advances in information technology and artificial intelligence have led to the design of autonomous UAVs, UGVs, USVs, and UUVs that can operate without, or with little, human intervention in their missions. Advances in UVs and their automated control have resulted in commercially available prototypes of different types of UVs. This, in turn, has created an unexplored potential for the applications of a wide variety of UVs to construction. This paper reviews this potential specifically for USVs, UUVs, and UGVs. The details of each mission, including application, UVs type and details of mission are described. The paper concludes with an identification of gaps in the current technology where developments are required before the full potential of USVs, UUVs, and UGVs can be achieved in construction.

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 a...

Unmanned surface vehicles (USVs) are remotely-operated robotic agents that can safely deploy and maneuver on the surface of bodies of water. Although, there is an established body of literature on large, complex and expensive USVs in structural health monitoring, comparing the solutions provided with the current available UAVs and their scale, cost, and applications, shows that USVs are not yet developed at this scale. The goal of this study is to provide a proof of concept for applications and development of USV devices (small, low cost, and versatile) similar to the current available UAVs. These devices can be used for monitoring the construction progress, facility management or in other words it can basically stretch over the lifecycle of the facilities. This paper first presents a discussion on different adaptations of the UVs and their definitions and applications. This is followed by a literature review on different health monitoring algorithms and methods to establish the body of knowledge in the area of automation of the health monitoring. This includes some related examples of small, low cost UAVs that are used to automate the health monitoring system. These are reviewed briefly to show how such systems can provide the basis for development of a similar USV. Next, deployment and use of various USVs is discussed. To provide a proof of concept for such a USV device an experiment is conducted with a small-scale commercial USV mounted with a camera to visually assess the health of a retention pond in Gainesville, Florida. The paper concludes with a discussion of the feasibility of the device and testing the possibility of automating the health assessment process using image-processing techniques.

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.