An Integrative Asset-Management Framework to Assist Post-Disaster Reconstruction in Post-Conflict Situations

Reconstruction is an essential measure for a society in a state of flux as a result of man-made- conflict. A long-term approach must become a priority and the guiding goal of reconstruction. Reconstruction rebuilds literally the infrastructure for damaged societies, and this process can succeed only if there is a national commitment to redevelopment of a fit-for- purpose built-environment as well as a commitment to involve the affected community’s facilities user-needs within an integrated asset-management framework

A System-of-Systems Framework for Exploratory Analysis of Climate Change Impacts on Civil Infrastructure Resilience

Climate change has various chronic and acute impacts on civil infrastructure systems (CIS). A long-term assessment of resilience in CIS requires understanding the transformation of CIS caused by climate change stressors and adaptation decision-making behaviors of institutional agencies. In addition, resilience assessment for CIS includes significant uncertainty regarding future climate change scenarios and subsequent impacts. Thus, resilience analysis in CIS under climate change impacts need to capture complex adaptive behaviors and uncertainty in order to enable robust planning and decision making. This study presented a system-of-systems (SoS) framework for abstraction and integrated modeling of climate change stressors, physical infrastructure performance, and institutional actors’ decision making. The application of the proposed SoS framework was shown in an illustrative case study related to the impacts of sea level rise and subsequent saltwater intrusion on a water system. Through the use of the proposed SoS framework, various attributes, processes, and interactions related to physical infrastructure and actor’s decision making were abstracted and used in the creation of a computational simulation model. Then, the computational model was used to simulate various scenarios composed of sea level rise and adaptation approaches. Through an exploratory analysis approach, the simulated scenario landscape was used to identify robust adaptation pathways that lead to a greater system resilience under future uncertain sea level rise. The results of the illustrative case study highlight the various novel capabilities of the SoS framework: (i) abstraction of various attributes and processes that affect the long-term resilience of infrastructure under climate change; (ii) integrated modeling of CIS transformation based on simulating the adaptive decision-making processes, physical infrastructure performance, and climate change impacts; and (iii) exploratory analysis and identification of robust pathways for adaptation to climate change impacts

A System-of-Systems Framework for Exploratory Analysis of Climate Change Impacts on Civil Infrastructure Resilience

Climate change has various chronic and acute impacts on civil infrastructure systems (CIS). A long-term assessment of resilience in CIS requires understanding the transformation of CIS caused by climate change stressors and adaptation decision-making behaviors of institutional agencies. In addition, resilience assessment for CIS includes significant uncertainty regarding future climate change scenarios and subsequent impacts. Thus, resilience analysis in CIS under climate change impacts need to capture complex adaptive behaviors and uncertainty in order to enable robust planning and decision making. This study presented a system-of-systems (SoS) framework for abstraction and integrated modeling of climate change stressors, physical infrastructure performance, and institutional actors’ decision making. The application of the proposed SoS framework was shown in an illustrative case study related to the impacts of sea level rise and subsequent saltwater intrusion on a water system. Through the use of the proposed SoS framework, various attributes, processes, and interactions related to physical infrastructure and actor’s decision making were abstracted and used in the creation of a computational simulation model. Then, the computational model was used to simulate various scenarios composed of sea level rise and adaptation approaches. Through an exploratory analysis approach, the simulated scenario landscape was used to identify robust adaptation pathways that lead to a greater system resilience under future uncertain sea level rise. The results of the illustrative case study highlight the various novel capabilities of the SoS framework: (i) abstraction of various attributes and processes that affect the long-term resilience of infrastructure under climate change; (ii) integrated modeling of CIS transformation based on simulating the adaptive decision-making processes, physical infrastructure performance, and climate change impacts; and (iii) exploratory analysis and identification of robust pathways for adaptation to climate change impacts

Balancing capital and condition : an emerging approach to facility investment strategy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2002.Includes bibliographical references (p. 145-152).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Capital facilities - land and buildings - provide a long-standing environment in which public and private enterprise works, communicates, and thrives. Aligning how facilities "fit" with the dynamic demands of enterprise necessitates continual investment in maintenance, modernization, and development. Conventional tools - condition assessment and master planning - provide a means for measuring this "fit" and producing related investment needs. These tools are often applied independently with a singular focus: condition assessment on physical systems and master planning on sizing and function. The resulting investment needs and condition metrics become fragmented elements of a larger (and often unstructured) investment context that must also consider funding realities and other strategic choices. Exceedingly the methods of collecting and managing assessment data are emphasized, while linkages to capital planning and decision-making remain narrowly focused and limited in scope. The result is simply greater volumes of more "bad news", as facility decision-makers are ill equipped to effectively synthesize numerous requirements and objectively understand the effects of investment decisions. This research develops and applies a new approach to facility investment strategy. The approach links the products of condition assessment and master planning, as well as ongoing facility costs, within a dynamic capital planning environment, where tradeoffs between present funding decisions and future conditions can be comprehensively explored.(cont.) Central to the approach is a conceptual framework that integrates investment needs and condition data within a broader planning context. A prototype tool is developed with the aid of information technology as a step toward implementation. The tool employs system-based cost models, aggregated deterioration models, financial-based condition metrics, and other facility cost modeling techniques to estimate present and future investment requirements and facility conditions. The tool is applied to two real facility portfolios within the U.S. Army Medical Department to demonstrate the feasibility and robustness of developing and evaluating investment strategies that balance capital, condition, and other strategic concerns. The application suggests a new direction for public and institutional capital allocation policy and asset accountability.by Stephen C. Wooldridge.Ph.D

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale

A risk mitigation framework for construction / asset management of real estate and infrastructure projects

The increasing demand on residential, office, retail, and services buildings as well as hotels and recreation has been encouraging investors from both private and public sectors to develop new communities and cities to meet the mixed demand in one location. These projects are huge in size, include several diversified functions, and are usually implemented over many years. The real estate projects’ master schedules are usually initiated at an early stage of development. The decision to start investing in infrastructure systems, that can ultimately serve fully occupied community or city, is usually taken during the early development stage. This applies to all services such as water, electricity, sewage, telecom, natural gas, roads, urban landscape and cooling and heating. Following the feasibility phase and its generated implementation schedule, the construction of the infrastructure system starts together with a number of real estate projects of different portfolios (retail, residential, commercial,…etc.). The development of the remaining real estate projects continues parallel to customer occupancy of the completed projects. The occurrence of unforeseen risk events, post completing the construction of infrastructure system, may force decision makers to react by relaxing the implementation of the remaining unconstructed projects within their developed communities. This occurs through postponing the unconstructed project and keeping the original feasibility-based sequence of projects unchanged. Decision makers may also change the sequence of implementing their projects where they may prioritize either certain portfolio or location zone above the other, depending on changes in the market demand conditions. The change may adversely impact the original planned profit in the original feasibility. The profit may be generated from either real estate portfolios and/or their serving Infrastructure system. The negative impact may occur due to possible delayed occupancy of the completed real estate projects which in turn reduces the services demand. This finally results in underutilization of the early implemented Infrastructure system. This research aims at developing a dynamic decision support prototype system to quantify impacts of unforeseen risks on the profitability of real estate projects as well as its infrastructure system in the cases of changing projects’ implementation schedules. It is also aimed to support decision makers with scheduled portfolio mix that maximizes their Expected Gross Profit (EGP) of real estate projects and their infrastructure system. The provided schedules can be either based on location zone or portfolio type to meet certain marketing conditions or even to respect certain relations between neighbor projects’ implementation constraints. In order to achieve the research objectives, a Risk Impact Mitigation (RIM) decision support system is developed. RIM consists mainly of four models, Real Estate Scheduling Optimization Model RESOM, Sustainable Landscape Optimization Model SLOM, District Cooling Optimization Model DCOM and Water Simulation Optimization Model WSOM. Integrated with the three Infrastructure specialized models SLOM, DCOM, WSOM, RESOM provides EGP values for individual Infrastructure systems. The three infrastructure models provide the demand profile that relate to a RESOM generated implementation schedule. RESOM then uses these profiles for calculating the profits using the projects’ capital expenditure and financial expenses. The three models included in this research (SLOM, DCOM and WSOM) relate to the urban landscape, district cooling and water systems respectively. RIM is applied on a large scale real estate development in Egypt. The development was subjected to difficult political and financial circumstances that were not forecasted while preparing original feasibility studies. RIM is validated using a questionnaire process. The questionnaire is distributed to 31 experts of different academic and professional background. RIM’s models provided expected results for different real life cases tested by experts as part of the validation process. The validation process indicated that RIM’s results are consistent, in compliance with expected results and is extremely useful and novel in supporting real estate decision makers in mitigating risk impacts on their profits. The validation process also indicated promising benefits and potential need for developed commercial version for future application within the industry

An Integrated Condition Assessment Model for Educational Buildings Using BIM

Building facilities compose a major part of any urban infrastructure. Despite their considerable economic, cultural and/or historic importance, several studies have shown that many buildings are sick, deteriorating and a major source of pollution. Maintaining a building is essential to keep it performing and functioning for a longer period of time as well as providing better quality of life for building occupants. Despite the importance of the condition assessment (CA) stage in the asset management process, literature review reveals that there is no building condition assessment framework that considers both physical and environmental conditions. Schools and educational facilities in Canada, which comprise a major component of the non-residential buildings sector, has passed 51% of their useful service life The primary objective of this research is to develop an Integrated Condition Assessment Model for Educational Buildings that considers both building physical and environmental conditions. This model will assist owners and facility managers in the condition assessment phase during the asset management process. As buildings are composed of spaces; this proposed model uses “space” as the principal element of evaluation. The Multi Attribute Utility Theory (MAUT) is used to calculate the physical and environmental conditions of each space, and the K-mean clustering is conducted to calculate the integrated condition of each one. Data are collected from experts via questionnaires to assign relative weights to models’ attributes using both the Analytical Network Process (ANP) and the Analytical Hierarchy Process (AHP) techniques. The proposed methodology upgrades the use of an object-oriented Building Information Model (BIM) so that it can be used as a platform and an advanced tool for storing, exchanging, and transferring assessment data inputs as well as serving in the assessment process. Integrated Condition Assessment model for Buildings (ICAB) is the developed automated tool that integrates with Revit© 2011. This integration allows the BIM model to be used as the data source and to provide any required graphical representation. The model is implemented and tested using data collected from experts and from field measurements taken from an educational building in Montreal. Finally, the model was validated by experts working in the facilities management field and they acknowledged having good potentials

Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management