Building Portfolio Research Articles

Risk Minimization for a Portfolio of Buildings Considering Risk Aversion

AbstractThis paper uses probabilistic models and minimization of certain risk measures to expose risk aversion and optimal retrofit strategies for an existing building portfolio. A case study is pr...

Probabilistic seismic vulnerability and loss assessment of the residential building stock in Costa Rica

This study presents a seismic vulnerability and risk assessment of the residential building stock in Costa Rica. It proposes a new exposure model using housing census data, public construction statistics, and private construction information to quantify and characterize the residential building portfolio. A complete vulnerability catalogue is established by developing fragility functions for the most common building classes and combining them with existing models derived for risk assessment in South America. An existing probabilistic seismic hazard model was implemented within the OpenQuake-engine, and complemented with a simplified site model to account for site effects. Earthquake risk assessment is achieved by means of a probabilistic event-based analysis, which allowed the estimation of several risk metrics. These include average annualized losses at a national scale, disaggregated per building class and administrative regions. The probable maximum losses and exceedance probability curves were generated using a stochastic event set with 100,000 years of events per logic tree branch.

Risk-Oriented, Bottom-Up Modeling of Building Portfolios With Faceted Taxonomies

Earthquake exposure describes the assets that are exposed to seismic activity and are susceptible to be damaged. In seismic risk applications, it mostly refers to the residential and commercial building portfolios, although in general may also include transport infrastructure and lifelines. Providing an efficient description of a complex urban environment in terms of the structural characteristics of buildings related to their seismic vulnerability is challenging, considering the variety of building practices, materials and configurations. A common approach entails the use of pre-defined building typologies, but this may introduce a bias in the resulting models. Faceted taxonomies have been recently introduced to provide a standardised description of buildings using a rich set of basic attributes, but cannot be used directly for risk-related applications. We argue that a bottom-up approach to exposure modelling might prove instrumental in increasing the quality and reliability of risk assessment, and propose hereby a novel score-based methodology to define and assign building classes to unclassified buildings in a sound and transparent way. The approach can be adopted for standard building classifications as well as for original typologies that may be more efficient in capturing the specific features of the building stock. The proposed methodology efficiently decouples the collection of buildings observations, typical of surveying activities, from the assignment of risk-aimed building classes, and provides a useful tool to practitioners and engineers involved in large-scale earthquake risk assessment. The proposed methodology has been exemplified with a building portfolio collected in France near the geothermal plant of Soultz-sous-F\^orets, and is used to rapidly characterise the seismic exposure of a built environment for induced seismicity applications.

Seismic Risk Mitigation for a Portfolio of Reinforced Concrete Frame Buildings through Optimal Allocation of a Limited Budget

The mitigation of seismic risk for a population of vulnerable civil critical structures (e.g., hospitals, schools, and bridges) is a crucial issue for many governments of earthquake-prone regions. Furthermore, owing to the global economic crisis, limited financial resources make full seismic rehabilitation of entire building stocks challenging. Therefore, a critical decision has to be made on the following key question: what is the most advantageous way of spending the available budget while treating each building in a portfolio differently, by giving it a different level of structural improvement to reduce the overall risk of the portfolio of buildings as much as possible? Herein, a decision-making tool is proposed to address this high-social-impact issue. Starting with a limited amount of information, which is gathered through expeditious surveys on existing buildings, and by involving uncertainties, the overall risk is evaluated from the fragility analysis of each structure. This is conducted via simplified pushover analyses by considering the local seismic hazard. Then, an optimization is performed for each building of the portfolio to select a relevant structural intervention from four alternatives (no intervention, partial retrofit, full retrofit, and demolition and reconstruction), based on both the overall risk reduction and the amount of financial resources. Procedures for quick estimation of fragility curves and installation costs are also discussed as part of the proposed approach. Finally, a practical application is presented with reference to a simulated case study consisting of 46 reinforced concrete school buildings located in Campania, Italy.

REDIS: A value-based decision support tool for renovation of building portfolios

Renovation of the existing building stock is getting increased attention in many European countries. One decision to be made by the professional building owner is the prioritization of which buildings to renovate within a building portfolio. This article proposes a new, value-based decision support tool, REDIS, which can support the professional building owner in this process. REDIS provides a framework for dialogue in the pre-design stage of renovation projects, and calculates a Renovation Value Factor for each building, based on a number of criteria weights, building status data and estimated renovation costs, indicating which buildings give the building owner most value for money if renovated. The REDIS tool is demonstrated through an application example using real data from a case of selecting which school buildings to renovate within a portfolio of 56 schools. Representatives from the municipality that owns the 56 schools compared the results of the REDIS tool to the results of their original decision process, where ten out of the 56 schools were chosen for renovation. The result from the tool differed from the original decision results. The evaluation results indicated that the REDIS tool supports the building owner in choosing which buildings to renovate within a building portfolio. The contributions of this study are the proposed decision support framework and the tool prototype, with the major contribution being to encourage a structured dialogue among the decision makers.

A methodology for auto-calibrating urban building energy models using surrogate modeling techniques

Owners of large building portfolios such as university campuses have long relied on building energy models to predict potential energy savings from various efficiency upgrades. Traditional calibration procedures for individual building model are time intensive and require specially trained personnel, making their applications to campuses with hundreds of buildings prohibitive. Recently proposed automatic calibration techniques reduce the manual effort during calibration but require hundreds of thousands of energy simulations which increase their cost. To reduce the computational effort of these methods, this paper proposes a methodology that uses a data-driven approximation technique. Instead of brute-force simulations using detailed engineering models, this study employs statistical surrogate models with an optimization algorithm to estimate properties of unknown building parameters. Results demonstrate that when envelope information is available, this workflow yields sufficiently accurate estimates of hard to observe building characteristics, about 500 times faster than traditional approaches.

Life Cycle Cost and Return on Investment as complementary decision variables for urban flood risk management in developing countries

Herein we investigate Life Cycle Cost (LCC) and Return on Investment (ROI) as potential decision variables for evaluating the economic performance (ROI) and financial feasibility (LCC) of a set of flood mitigation strategies over time. The main novelty of this work is the application of LCC and ROI analyses at the urban level to an asset portfolio of flood-prone buildings. Reduced flood damage is treated probabilistically as avoided costs (LCC analysis) and returns (ROI analysis), respectively. The proposed methodology is applied to the case of Dar es Salaam, Tanzania, which suffers severe riverine flooding on a sub-annual basis. Specifically, LCC and ROI of five mitigation scenarios that include large-scale catchment rehabilitation, settlement set-backs and waste management are compared with the current situation. The main result is that the highest-performing flood mitigation option includes both conventional interventions and ecosystem rehabilitation.

Effects of Epistemic Uncertainty in Seismic Hazard Estimates on Building Portfolio Losses

In catastrophe risk modeling, a defensible estimation of impact severity and its likelihood of occurrence to a portfolio of assets can only be made through a rigorous treatment of uncertainty and the consideration of multiple alternative models. This approach, however, requires repeating lengthy calculations multiple times. To limit the demand on computational time and resources, a frequent practice in the industry is to estimate the distribution of earthquake-induced portfolio losses using a simulated catalog of events from a single representative mean ground motion hazard model for the region. This simplified approach is faster but may provide biased estimates of the likelihood of occurrence of the large and infrequent losses that drive many risk mitigation decisions. Investigation through case studies of different portfolios of assets located in the San Francisco Bay Region shows the potential for both a bias in the mean loss estimates and an underestimation of their central 70% interpercentile. We propose a simplified and computationally practical approach that reduces the bias in the mean portfolio loss estimates. This approach does not improve the estimate of the inter-percentile range, however, a quantity of no direct practical use.

Enabling Cooperative Behavior for Building Demand Response Based on Extended Joint Action Learning

This paper explores the use of distributed intelligence to assist the integration of the demand as a flexible resource, to mitigate the emerging uncertainty in the power system, while fulfilling the customer's local needs, i.e., comfort management. More exactly, our contribution is twofold. First, we propose a novel cooperative and decentralized reinforcement learning method, dubbed extended joint action learning (eJAL). Second, we perform a comparison between eJAL to noncooperative decentralized decision making strategies, i.e., Q-learning, and a centralized game theoretic approach, i.e., Nash n-player game. This comparison has been conducted on the basis of grid support effectiveness and the loss of comfort for each customer. Various metrics were used to analyze the advantages and disadvantages of each method. We demonstrated that a range of flexibility requests can be met by providing an optimal energy portfolio of buildings without substantially violating comfort constraints. Moreover, we showed that the proposed eJAL method achieves the highest fairness index.

Risk-Based Prioritization of a Building Portfolio for Retrofit

AbstractThis paper puts forward a risk-based approach to prioritize a portfolio of buildings for retrofit. A new set of local sensitivity measures is proposed to rank the buildings based on the opt...

Estimation of Seismic Loss for a Portfolio of Buildings under Bidirectional Horizontal Ground Motions due to a Scenario Cascadia Event

Earthquake ground motions induced by a scenario event are spatially (partially) correlated and (partially) coherent. Simulated ground motion records can be used to carry out nonlinear inelastic time history analysis for a portfolio of buildings to estimate the seismic loss, which is advantageous as there is no need to develop and apply empirical ground motion prediction equations and the ductility demand rules, or to search the scenario compatible recorded records at selected sites that may not exist. Further, if the structures being considered are sensitive to the orientation of the excitation, multiple-component ground motion records are needed. For the simulation of such ground motion records, previous studies have shown that correlation and coherency between any pair of ground motion components need to be incorporated. In this study, the seismic loss of a portfolio of hypothetical buildings in downtown Vancouver under bidirectional horizontal ground motions due to a scenario Cascadia event is estimated by using simulated bidirectional ground motion records that include realistic correlation and coherency characteristics. The hysteretic behaviours of the buildings are described by bidirectional Bouc-Wen model. The results show that the use of unidirectional ground motions and single-degree-of-freedom (SDOF) system structural model may underestimate the aggregated seismic loss.

Probabilistic disaggregation of a spatial portfolio of exposure for natural hazard risk assessment

ABSTRACTIn natural hazard risk assessment situations are encountered where information on the portfolio of exposure is only available in a spatially aggregated form, hindering a precise risk assessment. Recourse might be found in the spatial disaggregation of the portfolio of exposure to the resolution of the hazard model. Given the uncertainty inherent to any disaggregation, it is argued that the disaggregation should be performed probabilistically. In this paper, a methodology for probabilistic disaggregation of spatially aggregated values is presented. The methodology is exemplified with the disaggregation of a portfolio of buildings in two communes in Switzerland and the results are compared to sample observations. The relevance of probabilistic disaggregation uncertainty in natural hazard risk assessment is illustrated with the example of a simple flood risk assessment.

Evaporite sinkhole risk for a building portfolio

Karst-related hazard can be a problem for buildings, especially in the case of evaporite karst. This study aims at evaluating the risk posed by evaporite karst for a building portfolio in western Switzerland, using a susceptibility map and an event inventory. Since the inventory is not complete, different corrections aim at obtaining a frequency of sinkhole events damaging a building as close as possible to the actual frequency. These corrections account for the variation of the building stock during the inventory period, the varying inventory quality among the municipalities and the partial knowledge, even in the best case. This approach is preferred here to estimating spatially the hazard, since the amount of information on the frequency and magnitude is insufficient to draw a proper hazard map. The distribution of loss ratios is also retrieved from the inventory, thanks to the estimated or actual price of the remedial works. Annual losses are then estimated using a Monte Carlo approach, which consists in sampling for a number of damaged buildings from a Poisson distribution, for a distribution of loss ratios and for a building value. Different exceedance curves relying on different hypotheses are presented, and the mean annual loss that the public insurance company might have to compensate is estimated at CHF 0.8–1.5 million.

Stochastic post-disaster functionality recovery of community building portfolios I: Modeling

Understanding the process of post-disaster recovery is a critical step toward assessing and achieving community resilience. However, the recovery process of a community building portfolio as a spatially distributed system is intrinsically complex and highly uncertain, and is conditional on the resourcefulness and social-economic characteristics of the community as well as the various decisions made by numerous community stakeholders and building owners at different phases of the recovery. We propose a simulation-based building portfolio recovery model (BPRM) to predict the functionality recovery time and recovery trajectory of a community building portfolio following natural scenario hazard events, in two steps: (1) modeling individual building-level restoration as a discrete state, continuous time Markov Chain (CTMC); and (2) modeling building portfolio-level recovery through aggregating the CTMC restoration processes of individual buildings across the domain of the community and over the entire recovery time horizon. We propagate uncertainties associated with the recovery process in a consistent manner in order to quantify portfolio recovery metrics probabilistically. The proposed building portfolio recovery model is intended to support risk-informed community resilience planning and hazard mitigation.

Stochastic post-disaster functionality recovery of community building portfolios II: Application

Part I of this two-part paper introduced a building portfolio recovery model (BPRM) to forecast the stochastic recovery process of building portfolios within a community following an extreme hazard event. The BPRM models restoration of individual building-level functionalities as a discrete state, continuous time Markov Chain, while it models portfolio-level recovery by aggregating the building-level restoration processes in both temporal and spatial dimensions. In this paper, we illustrate the implementation of the BPRM to project the building portfolio recovery of a mid-size community following a predefined scenario earthquake. The recovery analysis integrates the engineering process of building reconstructions with available community resources and social-economic characteristics of different population groups. The spatial variations in recovery speed in different residential zones within the community reflect the disparities in resourcefulness and recoverability of homeowners with different social and economic status. In addition, sensitivity studies are performed to investigate the capability of the BPRM to quantify the impact of candidate pre-disaster mitigation strategies on the building portfolio recovery time and trajectory, illustrating the potential use of the BPRM in supporting risk-informed community resilience planning.

A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency

This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings’ energy efficiency.

Probabilistic life-cycle cost-benefit analysis of portfolios of buildings under flood hazard

Nowadays, the number of residential buildings in flood prone zones is increasing significantly. Therefore, for these buildings, it is of vital importance to apply hazard risk mitigation strategies. In this paper, probabilistic cost-benefit analysis of flood risk mitigation strategies for portfolios of residential buildings is performed in a life-cycle context. Additionally, an efficient methodology is proposed to aid decision-makers on whether or not to retrofit portfolios of structures considering both expected and standard deviation of life-cycle cost. The vulnerability model of buildings under flood hazard is introduced considering serviceability and ultimate limit states. Probabilistic cost-benefit analysis is performed by comparing the effectiveness of different retrofit actions. Additionally, uncertainty and correlation effects are considered in the probabilistic loss and cost-benefit analysis procedures. The proposed approach is applied to a portfolio of residential buildings located in Florida to illustrate its capability and application. However, the proposed approach is general and can be applied to buildings located in other regions.

Measuring and enhancing resilience of building portfolios considering the functional interdependence among community sectors

Resilience is an attribute of communities, and is supported by community building sectors (occupancy types) with different functionalities. Evaluating community resilience and functionality requires the establishment of new metrics and their quantification. This study introduces a methodology to consider how the interdependencies in functionality among different building sectors impact community resilience. Four building sectors that provide essential functions to a community, i.e. housing, education, business and public services, are considered. The percentage of people in a community who dislocate following a disaster as a result of the physical damages to buildings is selected as the resilience metric in this conceptual study. A framework is further developed to determine the optimum strategies for retrofitting community building portfolios as a whole in order to achieve an overall community resilience objective expressed in terms of the threshold value of the community resilience metric identified above. Finally, the methodology to quantify community functionality and the associated retrofit optimization algorithm are illustrated using a simplified hypothetical community building portfolio in China exposed to potentially severe earthquakes, in which the objective is to achieve a predetermined functionality level when financial constraints may be present.

Estimating building vulnerability to volcanic ash fall for insurance and other purposes

Volcanic ash falls are one of the most widespread and frequent volcanic hazards, and are produced by all explosive volcanic eruptions. Ash falls are arguably the most disruptive volcanic hazard because of their ability to affect large areas and to impact a wide range of assets, even at relatively small thicknesses. From an insurance perspective, the most valuable insured assets are buildings. Ash fall vulnerability curves or functions, which relate the magnitude of ash fall to likely damage, are the most developed for buildings, although there have been important recent advances for agriculture and infrastructure. In this paper, we focus on existing vulnerability functions developed for volcanic ash fall impact on buildings, and apply them to a hypothetical building portfolio impacted by a modern-day Tambora 1815 eruption scenario. We compare and contrast the different developed functions and discuss some of the issues surrounding estimation of potential building damage following a volcanic eruption. We found substantial variability in the different vulnerability estimates, which contribute to large uncertainties when estimating potential building damage and loss. Given the lack of detailed and published studies of building damage resulting from ash fall this is not surprising, although it also appears to be the case for other natural hazards for which there are far more empirical damage data. Notwithstanding the potential limitations of some empirical data in constraining vulnerability functions, efforts are required to improve our estimates of building damage under ash fall loading through the collection of damage data, experimental testing and perhaps theoretical failure analysis. For insurance purposes, the current building typologies provided for use with vulnerability functions are too detailed to map to the relatively limited information on building types that is typically available to insurers. Thus, efforts to provide vulnerability functions that can be used where only limited information is available regarding building types would also be valuable, both for insurers and for at-risk areas that have not been subject to detailed building vulnerability surveys.

Site dependence and record selection schemes for building fragility and regional loss assessment

SummaryWhen performing loss assessment of a geographically dispersed building portfolio, the response or loss (fragility or vulnerability) function of any given archetype building is typically considered to be a consistent property of the building itself. On the other hand, recent advances in record selection have shown that the seismic response of a structure is, in general, dependent on the nature of the hazard at the site of interest. This apparent contradiction begs the question: Are building fragility and vulnerability functions independent of site, and if not, what can be done to avoid having to reassess them for each site of interest? In the following, we show that there is a non‐negligible influence of the site, the degree of which depends on the intensity measure adopted for assessment. Employing a single‐period (e.g., first‐mode), spectral acceleration would require careful record selection at each site and result to significant site‐to‐site variability of the fragility or vulnerability function. On the other hand, an intensity measure comprising the geometric mean of multiple spectral accelerations considerably reduces such variability. In tandem with a conditional spectrum record selection that accounts for multiple sites, it can offer a viable approach for incorporating the effect of site dependence into fragility and vulnerability estimates. Copyright © 2017 John Wiley & Sons, Ltd.