Despite recent technological advancements in the architecture, engineering, and construction (AEC... more Despite recent technological advancements in the architecture, engineering, and construction (AEC) industries, the number of fire-caused fatalities in residential and commercial buildings in the United States over the last decade has consistently been an order of magnitude higher than fatalities caused by all other types of natural disasters combined. In this research, EvacuSafe is developed as a tool for designers to allow for layout optimization of buildings and facilities in terms of their evacuation safety performance. Contributions of this research include the development and validation of two spatiotemporal risk indices to quantify the safety of the egress routes and individual building compartments. Other unique advantages of EvacuSafe include the seamless integration of fire dynamics simulation, agent-based crowd simulation and building information models (BIM) using IFC data structures, allowing EvacuSafe to be readily used by designers to analyze a building layout design under various fire scenarios and for layout design optimization based on multiple safety criteria. The development of the framework, the risk indices, and the IFC-based integration is illustrated and validated through a case study on a two-story office building with 59 compartments. The results of the implementation show that the EvacuSafe is a valuable tool for evacuation design and planning that provides a more comprehensive evaluation of the evacuation performance in comparison to the existing indices and safety measures in the industry.
The 2013 report card of America's infrastructure has scored the condition of oil and gas pipeline... more The 2013 report card of America's infrastructure has scored the condition of oil and gas pipelines as Dþ which means that such pipelines are in a relatively poor condition. More than 10,000 failures have been recorded in the US. These failures have resulted in environmental, health and property damages. Therefore, there is a definite need to give more attention to the maintenance of oil and gas pipelines. This paper develops a comprehensive model for the maintenance planning of oil and gas pipelines. The model selects rehabilitation/repair alternatives for oil and gas pipelines based on their condition during their service life. These alternatives are then used to calculate the cash flow throughout the service life of these infrastructures. The model, which uses Monte Carlo simulation and fuzzy approach to address the uncertainties in the estimation of the maintenance operation costs and the economic parameters, calculates the Equivalent Uniform Annual Worth of the identified alternatives. The optimum maintenance programmes consist of the alternatives that have the lowest life cycle cost of oil and gas pipelines. The model is expected to support pipeline operators in the maintenance decisionmaking process of oil and gas pipelines.
The aim of the present work is to evaluate the potential of various developed neural network mode... more The aim of the present work is to evaluate the potential of various developed neural network models to provide reliable predictions of PM10 hourly concentrations, a task that is known to present certain difficulties. The modeling study involves 4 measurement locations within the Greater Athens Area which experiences a significant PM-related air pollution problem. The PM10 data used cover the
The recent trend of movement and impetus towards realization of smart cities and smart multi-purp... more The recent trend of movement and impetus towards realization of smart cities and smart multi-purpose complexes calls for more efficient and safer disaster management systems. Elevators, as the main tool of vertical transportation in high-rise buildings, can potentially propel the advancements towards more competent disaster management systems. Dispatch algorithm of elevators is the dominant factor that determines how smart and efficient they could perform. With recent innovative research on checking the possibility of elevator use for emergency evacuation, the importance of having an expert control system, which would provide a faster and a safer evacuation program, has been observed. In this paper, the importance and feasibility of using Occupant Evacuation Elevators (OEEs) are reviewed. Also, a Smart Disaster Management System (SDMS) is proposed. The main purpose of this system is to simulate all the possible scenarios of emergency in the building and then, through the decision-making capability of the system, select the fastest and safest strategy of egress. To this end, an Agent-Based Modeling (ABM) unit is connected to an Artificial Intelligent (AI) unit to build a thinking engine for the proposed model. Overall, the paper shows how recent technologic advancements can be incorporated in order to form a smart disaster management system.
Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computi... more Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques.
Despite recent technological advancements in the architecture, engineering, and construction (AEC... more Despite recent technological advancements in the architecture, engineering, and construction (AEC) industries, the number of fire-caused fatalities in residential and commercial buildings in the United States over the last decade has consistently been an order of magnitude higher than fatalities caused by all other types of natural disasters combined. In this research, EvacuSafe is developed as a tool for designers to allow for layout optimization of buildings and facilities in terms of their evacuation safety performance. Contributions of this research include the development and validation of two spatiotemporal risk indices to quantify the safety of the egress routes and individual building compartments. Other unique advantages of EvacuSafe include the seamless integration of fire dynamics simulation, agent-based crowd simulation and building information models (BIM) using IFC data structures, allowing EvacuSafe to be readily used by designers to analyze a building layout design under various fire scenarios and for layout design optimization based on multiple safety criteria. The development of the framework, the risk indices, and the IFC-based integration is illustrated and validated through a case study on a two-story office building with 59 compartments. The results of the implementation show that the EvacuSafe is a valuable tool for evacuation design and planning that provides a more comprehensive evaluation of the evacuation performance in comparison to the existing indices and safety measures in the industry.
The 2013 report card of America's infrastructure has scored the condition of oil and gas pipeline... more The 2013 report card of America's infrastructure has scored the condition of oil and gas pipelines as Dþ which means that such pipelines are in a relatively poor condition. More than 10,000 failures have been recorded in the US. These failures have resulted in environmental, health and property damages. Therefore, there is a definite need to give more attention to the maintenance of oil and gas pipelines. This paper develops a comprehensive model for the maintenance planning of oil and gas pipelines. The model selects rehabilitation/repair alternatives for oil and gas pipelines based on their condition during their service life. These alternatives are then used to calculate the cash flow throughout the service life of these infrastructures. The model, which uses Monte Carlo simulation and fuzzy approach to address the uncertainties in the estimation of the maintenance operation costs and the economic parameters, calculates the Equivalent Uniform Annual Worth of the identified alternatives. The optimum maintenance programmes consist of the alternatives that have the lowest life cycle cost of oil and gas pipelines. The model is expected to support pipeline operators in the maintenance decisionmaking process of oil and gas pipelines.
The aim of the present work is to evaluate the potential of various developed neural network mode... more The aim of the present work is to evaluate the potential of various developed neural network models to provide reliable predictions of PM10 hourly concentrations, a task that is known to present certain difficulties. The modeling study involves 4 measurement locations within the Greater Athens Area which experiences a significant PM-related air pollution problem. The PM10 data used cover the
The recent trend of movement and impetus towards realization of smart cities and smart multi-purp... more The recent trend of movement and impetus towards realization of smart cities and smart multi-purpose complexes calls for more efficient and safer disaster management systems. Elevators, as the main tool of vertical transportation in high-rise buildings, can potentially propel the advancements towards more competent disaster management systems. Dispatch algorithm of elevators is the dominant factor that determines how smart and efficient they could perform. With recent innovative research on checking the possibility of elevator use for emergency evacuation, the importance of having an expert control system, which would provide a faster and a safer evacuation program, has been observed. In this paper, the importance and feasibility of using Occupant Evacuation Elevators (OEEs) are reviewed. Also, a Smart Disaster Management System (SDMS) is proposed. The main purpose of this system is to simulate all the possible scenarios of emergency in the building and then, through the decision-making capability of the system, select the fastest and safest strategy of egress. To this end, an Agent-Based Modeling (ABM) unit is connected to an Artificial Intelligent (AI) unit to build a thinking engine for the proposed model. Overall, the paper shows how recent technologic advancements can be incorporated in order to form a smart disaster management system.
Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computi... more Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques.
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Papers by Farid Mirahadi