Building Portfolio Research Articles

Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Environmental effects on the experimental modal parameters of masonry buildings: experiences from the Italian Seismic Observatory of Structures (OSS) network

AbstractThe paper presents an in-depth analysis of the ambient dynamic behavior of nine masonry buildings monitored by the Italian Seismic Observatory of Structures (OSS). Addressing a significant knowledge gap affecting this structural type, the study reveals how daily and seasonal fluctuations in environmental factors have a notable influence on its experimental modal parameters. A robust frequency-domain tracking algorithm is first developed to identify and follow the evolution of modal parameters over time, exploiting ambient vibration recordings acquired at sub-daily intervals on the structures. The procedure is systematically applied to the entire portfolio of case-study buildings and, in the first year of training, integrated with measurements of environmental parameters provided by nearby weather stations. The multivariate regression analysis indicates that temperature variation is the primary driver of the observed wandering of natural frequencies. The frequency–temperature relationship shows a positive correlation above zero degrees and, in several cases, a significant degree of nonlinearity already present in low-frequency global modes. Simple predictive models are proposed to address such nonlinear behavior, including freezing conditions and accounting for internal heating during winter. Leveraging these novel insights, the work develops strategies to improve the efficiency of data acquisition protocols and training periods, enabling the near-future extension of real-time condition assessment methodologies to the entire OSS network.

A practical method for functional recovery analysis based on seismic resilience assessment of city building portfolios

Enhancing the seismic resilience of city building portfolios represents a pivotal strategy for mitigating the adverse effects of earthquakes. A practical method for functional recovery analysis is proposed to quantitatively assess the seismic resilience of city building portfolios. The proposed method involves quantifying delay time and developing repair scheduling schemes. The delay time considers the dependencies of normal functioning buildings on infrastructure service availability and road accessibility. It emphasizes the varying demands of buildings for critical versus general infrastructure services. The repair scheduling scheme, conducted in two steps, employs a rational repair priority index that integrates the post-earthquake residual functionality, service capacity, and relative importance of buildings. This scheme prioritizes the repair of life-rescue-related buildings, distinguishes the repair sequence for different occupancy types of buildings, and reflects the principles of local governments in repair decision-making. The effectiveness of this practical method is demonstrated through a case study. Recovery curves are compared under different earthquake scenarios and levels of labor force constraints. The concept of normalized time is introduced to allow for a comparative assessment of seismic resilience across different recovery scenarios in a more practical manner. The results can provide valuable guidance for formulating pre-earthquake risk reduction strategies, developing post-earthquake functional recovery schemes, and constructing more resilient cities.

Real-time seismic damage simulation for urban building portfolio based on basic building information and machine learning

Timely and accurate simulation of seismic damage of the urban building portfolio is essential for pre-earthquake planning and post-earthquake rescue. This study proposes a real-time seismic damage simulation method for the urban building portfolio based on the multiple degree-of-freedom (MDOF) shear and flexural-shear model established by basic building information and machine learning. The formulas for predicting periods of six types of structures are proposed, and the surrogate model-I for predicting the critical inter-story hysteretic parameters are established to optimize the MDOF models. The surrogate model-II is established to rapidly predict the engineering demand parameters and damage states of structures under earthquakes. Regarding the generalization capability, an incremental learning strategy is advised to guarantee the accuracy of the surrogate model-II in predicting the seismic damage of other urban building portfolios. The results in two city-scale cases show that the proposed method can achieve real-time seismic damage simulation of urban building portfolio with acceptable accuracy, and it's not necessary to retrain or only needs minor additional training on the existing surrogate model when applied to other urban building portfolios.

Fragility and vulnerability models for poorly-detailed infilled buildings accounting for building-to-building and masonry infill variability

While in poorly-detailed masonry-infilled reinforced concrete (RC) buildings the significance of masonry infills is acknowledged, less attention has been paid to the variability surrounding their mechanical properties and its potential impact on fragility and, consequently, vulnerability models to be used in seismic risk estimation. This study addresses this gap by proposing updated fragility and vulnerability models of existing poorly-detailed masonry infilled and pilotis RC frames that consider the variability in building layout and infill properties. A large building portfolio, representative of those designed according to Italian codes between 1970 and 1980, is selected as case study, incorporating building-to-building variability through various geometrical configurations. Updated modelling uncertainty parameters, specifically derived for existing infilled RC frames, are used, including the variability surrounding the properties of different infill typologies and the occurrence of RC frame shear failure. The portfolio was analysed through multiple-stripe analyses, followed by the development of upgraded fragility models for buildings with different heights and geometrical configurations. Finally, the corresponding vulnerability models were derived using damage-to-loss models calibrated with post-earthquake observation damage data from the 2009 L'Aquila earthquake. The updated fragility and vulnerability curves can be used for more refined seismic risk estimates of large building portfolios, that account for building-to-building and infill-to-infill variability, revised modelling uncertainty estimates and a more robust intensity measure (AvgSa).

Optimizing the decarbonization of a geographically dispersed building portfolio

Many organizations are actively pursuing decarbonization goals to help mitigate the effects of climate change. This situation often requires capital replacement of building components with different fuel or higher efficiency variants to reduce emissions. However, this problem is complicated when an organization operates across a wide geographic area due to regional differences in weather, utility costs, and electrical grid emissions. This paper develops a framework for this problem by implementing energy, emissions, cost, and decision models that can scale across large organizations. The resulting decisions show the ability to reach decarbonization goals with lower costs and better rates of return. Compared to condition or unit-capacity ranking strategies, case studies show that the developed model reduces emissions by as much as 68.99 % or costs by as much as 47.08 %, depending on organizational goals.

Weather effects on the lifecycle of U.S. Department of Defense equipment replacement (WELDER)

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

Seismic risk model for regional buildings that considers the influence of temperature and intensity measures

Probability-based seismic risk and random ground motion theories are used to develop high-precision seismic risk functions and models of regional building portfolios. Seismic intensity measures and structural demand parameters are critical in developing seismic fragility models for building clusters. Earthquake damage data indicate that ambient temperature may influence structural fragility, but the effect of temperature on seismic risk is commonly ignored. A method for quantifying seismic intensity is proposed in this study and optimized using 450,000 acceleration records detected by 15 stations during the Luding earthquake in Sichuan, China, on September 5, 2022. Considering the influence of different temperature fields, the proposed seismic intensity quantification method was used to evaluate structural damage during two destructive earthquakes in Xinjiang, China (the Aktao earthquake on September 2, 2003 and the Zhaosu earthquake on December 1, 2003). Three structural empirical seismic risk matrices and cloud maps were developed, and a vulnerability membership curve was generated using fuzzy decision-making, a membership algorithm, and nonlinear regression. A structural seismic risk index function was proposed, and vulnerability cloud diagrams and curves based on actual structural seismic vulnerability datasets were generated. Together, these analyses demonstrated the impact of temperature and intensity measures on the seismic risk to regional buildings. A model was established to evaluate the seismic risk to typical structures and was validated using historical earthquake damage data from China. This method improves the accuracy of earthquake risk and vulnerability assessments for typical building clusters given the influence of temperature. The developed seismic risk prediction model enables the evaluation of vulnerability and resilience of typical structures under low and high temperatures.

A novel method for post-earthquake functional evaluation of city building portfolios

Enhancing the seismic resilience of city building portfolios is crucial to mitigating the impact of seismic disasters. A novel method is proposed to evaluate the post-earthquake functionality of city building portfolios, which can serve as the foundation for assessing the seismic resilience of cities. This method incorporates multiple dimensions of functional evaluation, ranging from structural and nonstructural components to city building portfolios, elucidating the mechanism of functional assemblies from lower to higher dimensions. The calculation of the physical functionality of individual buildings relies on the functionalities of their structural and nonstructural components, which are determined using the functional mapping method. The impacts of infrastructure failures on the functionality of individual buildings are explored using the hierarchical belief rule base inference method based on the evidential reasoning algorithm (BRBI-ER). A technique that combines Point of Interest analysis and visual interpretation is utilized to classify building portfolios into three types: single-function building portfolios, multi-function building portfolios, and integrated-function building portfolios. A cumulative functional correlation model and an integrated functional correlation model using BRBI-ER are established to evaluate the functionality of these types of building portfolios. The results can contribute to predicting the risk of functional losses before earthquakes, guiding the recovery process after earthquakes, and taking proactive measures to improve the seismic resilience of cities.

Building renovation plan - introducing energy and cost into the managerial perspectives: A case study

Implementing more stringent energy requirements for public buildings has the potential to expedite the renovation rate of building stocks. While many studies have explored optimal renovation solutions to reduce energy consumption and cost, few have established a comprehensive building portfolio strategy from the perspective of portfolio managers. This study addresses this gap by developing a framework that empowers building portfolio managers to make informed decisions regarding building renovations. Through an in-depth case study, the current workflow for determining renovation needs, which involves analyzing maintenance requirements, building usability, and adaptability, is enhanced by incorporating a cost-optimal renovation framework. The study utilizes building simulations and life cycle cost analysis to assess the energy reduction potential of six school buildings requiring renovation. The results demonstrate a potential for energy use reduction, ranging from 11% to 44% when incorporating energy-saving measures into the renovations. Cost-optimal solutions are achieved for two buildings, resulting in energy reduction ranging from 31% to 36% and cost reduction ranging from 7% to 8%. The primary contribution of this research is developing a framework that reduces ambiguities of which buildings a portfolio manager should prioritize for renovation. By identifying current performance and improvement potential, the framework enables benchmarking against energy performance classes, aiding in strategic decision-making. However, the study also underscores the complexities of assessing a diverse building stock from a bottom-up perspective. Barriers such as obtaining high-quality data, scaling assessments, time constraints, and knowledge transfer are problems to overcome. Additionally, evaluating various building typologies with diverse characteristics, including those with legal restrictions due to heritage status, presents unique challenges. Future research directions should focus on expanding the framework to incorporate a life cycle assessment perspective. This approach will ensure a comprehensive building portfolio strategy that mitigates the risk of sub-optimal solutions.

The John Mitchell Lecture 2023: road asset, socio-economic and fatality risk from debris flow

Fast-moving, rainfall-induced debris-flow events are relatively common in the mountainous areas of the UK. The mechanisms of such debris flows are considered as they bridge between slow mass movements and flood phenomena. A series of case studies of debris-flow impacts from Scotland and overseas is described. The assessment of landslide hazards and risks are essential precursors to landslide risk reduction. This is particularly the case when an authority is responsible for an infrastructure or building portfolio that may be affected by multiple hazards. A framework for risk acceptance is used to set the context, and the use of a semi-quantitative assessment to determine the sites of highest risk is described. These highest risk sites are subject to quantitative risk assessments for road-user fatalities as a result of debris flows. A novel approach is taken to assess the socio-economic risks and the use of fragility curves to articulate the vulnerability of road infrastructure, including the newly developed approach involving systems of assets, is also described. The effects of climate change are considered alongside likely social and/or demographic change. A strategic approach to landslide risk reduction is used to illustrate how a clear focus on the overall goal of risk reduction can be beneficial in developing an effective strategy before homing in on the desired outcomes and the generic approach to achieving those outcomes.

Optimization of Personalized Mass Customization Building Portfolio by Integrating BIM and NSGA Algorithms

The objective of this study is to optimize the personalized mass customization building portfolio by using BIM and NSGA algorithms to achieve the desired balance between cost and efficiency. The study uses BIM software for cost simulation, and NSGA-II algorithm for multi-objective optimization. The mathematical model of building cost and duration is constructed and the multi-objective optimization algorithm is applied to achieve optimization under multiple objectives. The results of the study show that after 20,000 generations of iterations, 191 Pareto solutions are determined to be valid, and the stability and uniform distribution of the optimization objectives are verified, with the percentage of valid solutions reaching 92\%. In the conducted VIKOR scenario analysis, three different cost-effective optimization schemes are proposed depending on user preferences. The method of integrating BIM and NSGA algorithm can effectively improve the cost-effectiveness of building design, and provides a feasible and efficient optimization path for personalized mass-customized buildings.

InSAR-based rapid damage assessment of urban building portfolios following the 2023 Turkey earthquake

Timely acquisition of disaster information and damage assessment are crucial for mitigating earthquake impact. However, obtaining post-earthquake information promptly is challenging due to the disruptions caused by earthquakes to human activities and the environment. Remote methods have been widely used for rapidly assessing post-earthquake damage owing to their convenience and high precision. Among these methods, Interferometric Synthetic Aperture Radar (InSAR) is renowned for its large area coverage capabilities. However, InSAR may not be suitable for monitoring horizontal deformation of buildings. This study introduces a rapid loss assessment method for post-earthquake building portfolios based on InSAR. Specifically, we obtained the deformation in the horizontal direction of building portfolios in Adıyaman and Nurdağı during the 2023 Turkey earthquake based on InSAR and evaluate the proposed method's effectiveness by comparing it with optical remote sensing imagery. The results demonstrate that this method is efficient and cost-effective in evaluating post-earthquake damage of building portfolios.

Enhancing indoor air quality in office buildings: Insight from a field study

Building tight for minimizing building energy consumption and related carbon emissions might negatively affect indoor air quality (IAQ) if this is not correctly designed and evaluated during operation. To address this concern, a post occupancy evaluation study was conducted in a recent large office building in the Autonomous Province of Bolzano (Italy). The aim of the study was to collect evidence from a relevant case study and use it as a basis to define a standardized strategy for continuous monitoring of a larger portfolio of office buildings. Additionally, carbon dioxide (CO2), particulate matter (PM2.5, PM10), total volatile organic compounds (TVOC), air temperature, and relative humidity were recorded 24/7 for a 5-week period in winter 2023. Detailed spot measurements of VOCs and aldehydes were also carried out. Perceived air quality (PAQ) was evaluated through satisfaction surveys which covered various factors influencing PAQ including 14 potential sources of dissatisfaction. A cross-modal approach allowed for a comprehensive examination of the different domains (thermal, visual, acoustic, and IAQ) on PAQ. Measured contaminants’ levels were below recommended limits. Overall, participants were neither satisfied nor dissatisfied with PAQ, and possible related sources of dissatisfaction were elements such as the relative humidity. Sick building syndrome (SBS) selfreported symptoms such as dry skin and itchy eyes confirmed potentially too low relative humidity levels.

A Probabilistic Framework for Resilience Quantification of Residential Building Portfolios Exposed to Tropical Cyclone Winds

A Probabilistic Framework for Resilience Quantification of Residential Building Portfolios Exposed to Tropical Cyclone Winds

An asynchronous simulation algorithm for post‐earthquake functional recovery of interdependent infrastructure systems

AbstractThe seismic resilience assessment of infrastructure systems not only requires evaluation of immediate damages after earthquakes, but also estimate of functional losses during the post‐earthquake recovery process. Infrastructure systems are interdependent during post‐earthquake recovery. To accurately consider this interdependence in resilience assessment, many studies have adopted post‐earthquake recovery simulation methods. In this study, an efficient asynchronous simulation algorithm is proposed, which does not require synchronous checking of the states of infrastructure elements at each time step, but rather asynchronously derives the functional recovery time of each element based on its dependency relationships. This study proves that the time complexity of the proposed asynchronous algorithm is proportional to the scale of the infrastructure system and is independent of the time step size. Furthermore, a case study of post‐earthquake recovery of a portfolio of buildings considering safety cordon areas is used to demonstrate that the asynchronous algorithm can significantly reduce the simulation time of large‐scale infrastructure systems compared to synchronous algorithms.

Post-earthquake fire ignition model uncertainty in regional probabilistic shaking–fire cascading multi-hazard risk assessment: A study of earthquakes in Japan

Fires following earthquakes have significant impact on urban communities in earthquake-prone countries with wooden buildings, in addition to the main earthquake hazard of ground shaking. Regional-scale post-earthquake fire risk assessments are important for developing effective risk reduction strategies. Such risk assessments require the consideration of epistemic uncertainty, that is, uncertainty caused by lack of knowledge concerning the best models, in addition to aleatoric uncertainty. This study focused on epistemic uncertainty in post-earthquake fire ignition estimations, and incorporated it into risk assessments by using alternative empirical ignition models. The key objectives of this study were as follows: (1) correlating the probability of ignition per person to the ground motion intensity based on data for different past large earthquakes in Japan; (2) investigating the impact of using ignition models calibrated on different earthquake events in risk assessments through a realistic case study considering possible large earthquakes. A hierarchical Bayesian Poisson regression analysis revealed that the empirical relationship between the ignition probability and ground motion intensity greatly varies across six major large earthquakes in Japan from 1995 to 2022. Particularly, the effects of ignition prevention measures that have been widely implemented in households since the 1995 Kobe earthquake were inferred from much smaller ignition probabilities in approximately the last 10 years. The effects of this high epistemic uncertainty manifested as the large variability of the loss exceedance curve for a portfolio of buildings. These results indicate that the new ignition models can help to foster risk-informed decision-making under epistemic uncertainty.

Impact of the Uncertainty in the Parameters of the Earthquake Occurrence Model on Loss Estimates of Urban Building Portfolios

Abstract Understanding the potential socioeconomic losses due to natural hazards, such as earthquakes, is of foremost importance in the field of catastrophe risk management. The construction of a probabilistic seismic risk model is complex and requires the tuning of several parameters essential to represent the seismic hazard of the region, the definition of the exposed inventory characteristics, and its vulnerability to ground motion. Because significant uncertainties could be associated with each model component, the loss estimates are often highly volatile. Nevertheless, to reduce the conceptual complexity and the computational burden, in many real-life applications these uncertainties are either not adequately treated or neglected altogether. The false high fidelity of the ensuing loss estimates can mislead decision-making strategies. Hence, it is useful to assess the influence that the variability in the estimated values of the model input parameters may exert on the final risk results and their relevant contributions. To this purpose, we have performed a sensitivity analysis of the results of an urban seismic risk assessment for Isfahan (Iran). Systematic variations have been applied to the values of the parameters that control the earthquake occurrence in the probabilistic seismic hazard model. Curves of input–output relative variations were built for different risk metrics with the goal of identifying the parameters most sensitive to input uncertainty. Our findings can be useful to support risk managers and practitioners in the process of building seismic hazard and risk models. We found that the Gutenberg–Richter a and b values, the maximum magnitude, and the threshold magnitude are large contributors to the variability of important risk measures, such as the 475 yr and the average annual loss, with the more frequent losses being, in general, most sensitive.

Seismic loss assessment for regional building portfolios considering empirical seismic vulnerability functions

Seismic loss assessment for regional building portfolios considering empirical seismic vulnerability functions

A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries

Post-disaster recovery is a significant challenge, especially in developing countries. Various technical, environmental, socioeconomic, political, and cultural factors substantially influence post-disaster recovery. As a result, methodologies relevant in developed nations may not be directly applicable in Global South contexts. This study introduces a probabilistic framework for modeling the post-disaster recovery of buildings and electric power networks (EPNs) in developing countries. The proposed framework combines an asset-level assessment of individual buildings with a community-level assessment of EPNs to evaluate a building portfolio's post-event functionality state. As part of the framework, a stochastic network analysis is proposed to estimate the recovery time of damaged buildings while accounting for technical, environmental, socioeconomic, political, and cultural factors, quantified using empirical data gathered from past events. A probabilistic modeling approach is proposed to quantify the EPN's initial post-event outage levels. Empirical formulations for estimating the recovery time of an EPN as a function of its initial post-event outage levels are calibrated using post-event data from developing countries. A case study is presented to illustrate the application of the proposed framework to model the post-earthquake recovery of a synthetic low-income residential community. The analysis shows that negative technical, environmental, socioeconomic, political, and cultural factors could amplify the reconstruction time of damaged buildings by a factor of almost three. The proposed framework can support decision-makers in disaster planning and management strategies for vulnerable low-income communities.