Farrokh Jazizadeh

Application Areas and Data Requirements for BIM-Enabled Facilities Management

Facilities management (FM) encompasses and requires multidisciplinary activities, and thus has extensive information requirements. While some of these needs are addressed by several existing FM information systems, building information modeling (BIM), which is becoming widely adopted by the construction industry, holds undeveloped possibilities for providing and supporting FM practices with its functionalities of visualization, analysis, control, and so on. This paper explores how BIM can be a beneficial platform for supplementing FM practices. An online survey and face-to-face interviews were conducted to assess the current status of BIM implementations in FM, potential applications, and the level of interest in the utilization of BIM. Interactions between BIM and FM are defined by illustrating application areas and data requirements for BIM-enabled FM practices. Highlighting the synergy between the two, this paper can help professionals recognize potential areas in which BIM can be useful in FM practices.

BIM-Enabled Virtual and Collaborative Construction Engineering and Management

Todays construction engineering and management (CEM) graduates must have strong communication and teamwork skills; they must have the ability to work efficiently within colocated teams; and finally, they must know how to apply fundamental engineering, man- agement, and computer skills in practice. However, the traditional CEM education does not equip future engineers and managers to deal successfully with such issues. The authors describe experiences from a course that focuses on modes of learning involving virtual collabo- ration, problem-oriented project-based learning, and role-based learning. The aim of this course is to combine experimental and experiential learning into a research driven experience. The course was codesigned and cotaught by two instructors from two universities. The learning outcomes and lessons learned during the introduction of this building information modeling (BIM)-enabled virtual and collaborative con- struction engineering and management course are discussed. Specifically, it is shown that the introduction of BIM in a virtual collaborative setting allows instructors to design a course that incorporates the use of more realistic scenarios that better simulate real-world challenges. Such experiences teach students how construction projects are executed in practice, how different disciplines rely on one other for infor- mation, what type of information is needed from relevant disciplines, and when and how this information could be exchanged/shared between tools and processes. DOI: 10.1061/(ASCE)EI.1943-5541.0000098.

Unsupervised Approach for Autonomous Pavement-Defect Detection and Quantification Using an Inexpensive Depth Sensor

AbstractCurrent pavement condition–assessment procedures are extensively time consuming and laborious; in addition, these approaches pose safety threats to the personnel involved in the process. In this study, a RGB-D sensor is used to detect and quantify defects in pavements. This sensor system consists of a RGB color image, and an infrared projector and a camera that act as a depth sensor. An approach, which does not need any training, is proposed to interpret the data sensed by this inexpensive sensor. This system has the potential to be used for autonomous cost-effective assessment of road-surface conditions. Various road conditions including patching, cracks, and potholes are autonomously detected and, most importantly, quantified, using the proposed approach. Several field experiments have been carried out to evaluate the capabilities, as well as the limitations of the proposed system. The global positioning system information is incorporated with the proposed system to localize the detected defects...

Continuous Sensing of Occupant Perception of Indoor Ambient Factors

Ambient factors such as temperature, lighting, and air quality influence occupants’ productivity and behavior. Although these factors are regulated by industry standards and monitored by the facilities management groups, occupants’ perceptions vary from actual values due to various factors such as building schedules and occupancy, occupant activity and preferences, weather and climate, and the placement of sensors. While occupant comfort surveys are sometimes conducted, they are generally limited to one-time or periodic assessments that do not fully represent occupant experiences throughout building operations. This study proposes a new methodology for gathering real time data on a continuous basis through participatory sensing of occupant ambient comfort in indoor environments based on a smart phone application. The developed application is presented and validated by a pilot study in a university building. Occupant perceptions of temperature are compared to actual temperature records. No correlation is found between perceived and actual room temperatures demonstrating the potential of a participatory sensing tool for adaptively controlling building temperature ranges.

Human-Building Interaction for Energy Conservation in Office Buildings

Buildings are one of the major consumers of energy in the U.S. Both commercial and residential buildings account for about 42% of the national U.S. energy consumption. The majority of commercial buildings energy consumption is attributed to lighting (25%), space heating and cooling (25%), and ventilation (7%). Several research studies and industrial developments have focused on energy management based on maximum occupancy. However, fewer studies, with the objective of energy savings, have considered human preferences. This research focuses on office buildings’ occupants’ preferences and their contribution to the building energy conservation. Accordingly, occupants of selected university campus offices were asked to reduce lighting levels in their offices during work hours. Different types of information regarding their energy consumption were provided to the occupants. Email messages were used to communicate with the occupants. To monitor behavioral changes during the study, the test bed offices were equipped with wireless light sensors. The deployed light sensors were capable of detecting variations in light intensity, which was correlated with energy consumption. The impact of different types of information on occupant’s energy related behavior is presented.

Personalized Thermal Comfort Driven Control in HVAC Operated Office Buildings

Occupant comfort is a dominant influence on the performance of HVAC operations. Most HVAC system operations rely on industry standards to ensure satisfactory environmental conditions during occupancy. Despite the increasing building energy consumption rates, occupants are not usually satisfied with indoor conditions in commercial buildings. To address this issue, in this paper, a framework for integrating personalized comfort preferences into HVAC control logic is introduced. As part of the framework, a user proxy, a comfort profile learning algorithm, and a building management system (BMS) controller are presented. The performance of the framework in a real building setting has been evaluated. The framework was successful in a small-scale experiment in increasing efficiency by improving user comfort and slight decrease in collective energy consumption.

A Novel Method for Non Intrusive Load Monitoring of Lighting Systems in Commercial Buildings

In the U.S., buildings account for 42% of the total energy consumption - half of which is consumed by commercial buildings. Knowledge of electricity use patterns in buildings has several applications in demand side management. In commercial buildings, acquiring energy consumption information with high granularity requires consuming node level sub-metering, which is prohibitively expensive. Accordingly, a cost effective, non-intrusive energy metering is needed to provide room level and personalized energy consumptions. This paper proposes a vision for an alternative non-intrusive load monitoring approach in commercial buildings. As part of the overall vision, the feasibility of non-intrusive lighting load monitoring using single wireless light intensity sensors has been explored. The experimental results show the feasibility of the vision introducing area of the room, the fluctuation amplitude of the captured signal, and the daily variation trend of the signal as some of the parameters and features that could be applied for machine-learning training purposes.

Effects of Color, Distance, and Incident Angle on Quality of 3D Point Clouds

In laser scanning, the precision of the point clouds (PC) acquisition is influenced by a variety of factors such as environmental conditions, scanning tools and artifacts, dynamic scan environments, and depth discontinuity. In addition, object color, object texture, and scanning geometry are other factors that affect the quality of point clouds. These factors can affect the overall quality of point clouds, which in turn could result in a significant impact on the accuracy of as-built models. This study investigates the effect of object color and texture on the PC quality using a time of flight scanner. The effect of these factors has investigated through an experiment carried out on the Rosenblatt Stadium in Omaha, Nebraska. The outcomes of this ongoing research will be used to further highlight the parameters that must be taken into consideration in 3D laser scanning operations to avoid sources of errors that result from laser sensor, object characteristics, and scanning geometry.

Online Learning for Personalized Room-Level Thermal Control: A Multi-Armed Bandit Framework

We consider the problem of automatically learning the optimal thermal control in a room in order to maximize the expected average satisfaction among occupants providing stochastic feedback on their comfort through a participatory sensing application. Not assuming any prior knowledge or modeling of user comfort, we first apply the classic UCB1 online learning policy for multi-armed bandits (MAB), that combines exploration (testing out certain temperatures to understand better the user preferences) with exploitation (spending more time setting temperatures that maximize average-satisfaction) for the case when the total occupancy is constant. When occupancy is time-varying, the number of possible scenarios (i.e., which particular set of occupants are present in the room) becomes exponentially large, posing a combinatorial challenge. However, we show that LLR, a recently-developed combinatorial MAB online learning algorithm that requires recording and computation of only a polynomial number of quantities can be applied to this setting, yielding a regret (cumulative gap in average satisfaction with respect to a distribution aware genie) that grows only polynomially in the number of users, and logarithmically with time. This in turn indicates that difference in unit-time satisfaction obtained by the learning policy compared to the optimal tends to 0. We quantify the performance of these online learning algorithms using real data collected from users of a participatory sensing iPhone app in a multi-occupancy room in an office building in Southern California.

A novel system for road surface monitoring using an inexpensive infrared laser sensor

In this study, an inexpensive depth sensor is used to identify defects in pavements. This depth sensor consists of an infrared projector and camera. An innovative approach is proposed to interpret the data acquired by this sensor. The proposed system in this study is a breakthrough achievement for autonomous cost-effective condition assessment of roads and transportation systems. Various road conditions including patching, cracks, and potholes can be robustly and autonomously assessed using the proposed approach. Several field experiments have been carried out to evaluate the capabilities of this system. The field tests clearly demonstrate the superior features of the developed system in this study compared to conventional approaches for pavement evaluation.