Mohsen Shahandashti

A Critical Review of Methods Used to Determine Productivity of Mechanical, Electrical, and Plumbing Systems Coordination

Mechanical, Electrical, and Plumbing (MEP) systems coordination is a process during the pre-construction phase through which the proposed location and route of each system components are specified. The MEP coordination process has significantly changed due to utilization of Building Information Modeling (BIM). There is a gap in knowledge regarding productivity measurement of MEP coordination team. Moreover, there is a need for research to enhance our understanding about important factors affecting productivity of MEP coordination. The main objectives of this research are: (i) to document approaches for conducting MEP coordination using BIM, (ii) to identify metrics for measuring productivity of MEP coordination; and (iii) to identify factors affecting MEP coordination productivity. A questionnaire survey was conducted to achieve these objectives. The survey show that the most frequently used metric for conducting MEP coordination using BIM is square feet of coordinated area per total coordination hour. Moreover, experience level of MEP coordination team members is the top factor that significantly affects MEP coordination productivity. The findings of this study indicate that construction industry lacks a systematic procedure to record information to track, measure, and compare MEP coordination productivity across different coordination projects.

A Real Options Model to Evaluate Investments in Photovoltaic (PV) Systems

Transformative technologies such as Photovoltaic (PV) system have promising features for substantial reductions in carbon emissions and environmental footprints of the building sector. Nevertheless, investment in PV systems requires substantial implementation costs followed by a long period of recovering the invested capital through savings in electricity bills. An appropriate investment valuation method is needed to conduct tradeoff analysis between electricity saving benefits and implementation costs, and find the proper investment values of PV systems. Currently, the valuation of investments in PV systems are made based on the conventional methods, such as payback period, return on investment (ROI) and net present value (NPV) (Muldavin 2010; Prindle and de Fontaine 2009). However, these methods have considerable limitations for the proper valuation of the investments in PV systems for buildings. The existing methods do not address two major issues that impact the value of such investments: uncertainty and timing. We present new approach towards the valuation of investments in PV system for buildings that challenges the theoretical foundation of current investment valuation methods. We utilize the Real Options Theory to overcome the limitations of existing investment valuation methods. We propose a novel investment valuation model that accounts for changes in the implementation costs of PV systems as well as uncertainty about their respective electricity saving benefits. This investment valuation model can be used to find the optimal time for implementing PV systems for buildings, as well as the financial value of properly-timed investments in PV systems.

Analysis of the Temporal Relationships between Highway Construction Cost and Indicators Representing Macroeconomic, and Construction and Energy Market Conditions

Significant changes in highway construction cost are problematic for cost estimation, bid preparation, and investment planning of highway construction projects in the United States. Analysis of the temporal relationships between highway construction cost and macroeconomic, and construction and energy market indicators helps to have a better understanding about highway construction cost changes within the U.S. macroeconomic, and energy and construction market context. This temporal analysis helps to identify leading indicators of highway construction cost. Leading indicators of highway construction cost are indicators which past values contain information that is useful for forecasting the future values of highway construction cost. The research objective is to analyze the temporal relationship between highway construction cost and macroeconomic, and construction and energy market indicators using multivariate time series tests. The dataset under study includes National Highway Construction Cost Index (NHCCI) provided by Federal Highway Administration (FHWA) and indicators representing macroeconomic condition (Producer Price Index, Gross Domestic Product (GDP), GDP Implicit Price Deflator, Dow Jones Industrial Average, Money Supply, Prime Loan Rate, Unemployment Rate, Federal Funds Rate, and Consumer Price Index), construction market condition (Number of Housing Starts, Number of Building Permits, Construction Spending, Average Hourly Earnings, Average Weekly Hours and Employment Rate in Construction) and energy market condition (Crude Oil Price). Research results show that crude oil price and average hourly earnings are the leading indicators of NHCCI. The research findings contribute to the state of knowledge by filling the gap in knowledge about the leading indicators of highway construction cost. The research findings contribute to the state of practice by helping cost estimators, budget planners and construction market analysts explain highway construction cost variations and forecast the future trends, in order to improve the accuracy of their budgets, estimates and bids.

Time and motion study for solar contractors

One of the main goals of the solar energy industry is making solar technologies cost-competitive with other forms of energy. In particular, balance-ofsystem (BoS) costs associated with the installation of photovoltaic (PV) systems require significant cost savings to stay competitive with conventional energy sources. Significant potential for cost savings exists through increasing the productivity of the labor force installing the system. A gap in knowledge exists regarding approaches to measuring and improving solar installation labor productivity. The objectives of this research are to design a proper method for conducting time and motion studies for solar contractors, collect and aggregate data related to installation practices, and examine factors that affect solar installation labor productivity. In order to achieve these objectives, the Georgia Technology Research Institute (GTRI) laboratory, in partnership with the Rocky Mountain Institute (RMI), conducted time and motion studies on residential and commercial projects sites to collect and document data on the steps in the installation process of PV systems. An aggregation and analysis of data recorded for each step in these studies was performed to estimate the time to accomplish each task and identify the patterns that affect labor productivity. For this research, the proposed time and motion study approach was examined on two residential and commercial solar installation projects. The findings of the research show that conducting proper time and motion studies can determine the time spent on solar PV installations activities, in order to better understand which activities consume the most labor time and what types of contextual variables have greater impacts on installation labor productivity.

A Data Envelopment Analysis Model for Building Energy Efficiency Benchmarking

Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. Several methods, such as Data Envelopment Analysis (DEA), were proposed to perform building energy benchmarking. However, existing DEA building energy benchmarking models are subject to several limitations. Current DEA models are sensitive to data outliers such that even a single outlier can result in dramatic changes in efficiency scores of all buildings in a peer group of buildings. In addition, current DEA models cannot determine whether change of energy-efficiency score of a building is result from the change of energy efficiency in the building itself or the change of energy efficiency in the peer group of buildings. The objective of this research is to create a peer-wise building energy benchmarking model based on a novel DEA method that is capable of selecting significant factors, detecting outliers in the peer group of buildings and decomposing changes in energy-efficiency into two components (self-efficiency change and peer-efficiency change). In order to achieve this objective, the following research process was constructed. We devised an approach based on Data Cloud Analysis to detect and remove outlier buildings that significantly impact the DEA benchmarking results. We formulated an innovative DEA benchmark model to calculate the peer-wise energy-efficiency score of buildings considering the impact of building scales on energy efficiency. We created an energy efficiency measure based on the Malmquist Index to decompose changes in the building energy efficiency score. We applied the proposed DEA model to benchmark energy efficiency of multifamily properties. The proposed benchmark model is beneficial to building owners and facility managers because it identifies underperforming buildings, sets these underperforming buildings as energy improvement priorities, and self-comparable efficiency changes.

Automatic fault detection for Building Integrated Photovoltaic (BIPV) systems using time series methods

Purpose Faults in the actual outdoor performance of Building Integrated Photovoltaic (BIPV) systems can go unnoticed for several months since the energy productions are subject to significant variations that could mask faulty behaviors. Even large BIPV energy deficits could be hard to detect. The purpose of this paper is to develop a cost-effective approach to automatically detect faults in the energy productions of BIPV systems using historical BIPV energy productions as the only source of information that is typically collected in all BIPV systems. Design/methodology/approach Energy productions of BIPV systems are time series in nature. Therefore, time series methods are used to automatically detect two categories of faults (outliers and structure changes) in the monthly energy productions of BIPV systems. The research methodology consists of the automatic detection of outliers in energy productions, and automatic detection of structure changes in energy productions. Findings The proposed approach is applied to detect faults in the monthly energy productions of 89 BIPV systems. The results confirm that outliers and structure changes can be automatically detected in the monthly energy productions of BIPV systems using time series methods in presence of short-term variations, monthly seasonality, and long-term degradation in performance. Originality/value Unlike existing methods, the proposed approach does not require performance ratio calculation, operating condition data, such as solar irradiation, or the output of neighboring BIPV systems. It only uses the historical information about the BIPV energy productions to distinguish between faults and other time series properties including seasonality, short-term variations, and degradation trends.