With the increasing number and diversity of disruptive events imposed on the built environment, it is becoming more important to identify a system's resilience. Quantifying resilience is crucial because it enables effective preparation, recovery, and adaptation to the uncertainties that lie ahead. This study focuses on the resilience of building facades, which play an integral role in a building's various functions, including environmental, structural, and operational performance. Facades contribute significantly to the total building damage, yet their resilience is not sufficiently addressed in current discourse. This research aims to bridge this research gap by addressing the question: How can the integrated resilience of a facade system to multiple hazards be identified? A methodology was developed with two objectives: 1) evaluating facade resilience, and 2) integrating this methodology into the facade design process.

The Resilience-based Facade Design Framework assesses the impact of multiple hazards on a building facade, taking into account its fragility. The framework provides a quantitative approach to decision-making regarding facades, both in the early design stage and in retrofitting. Users can input project location, building geometry, and existing facade specifications to create a facade package. This package is then assessed for resilience under different hazards, including seismic and heat hazards. The resilience performance, defined in terms of resilience loss and economic loss, is integrated into a multi-attribute decision-making tool. This tool allows users to select a facade package based on its integrated resilience performance value, or to configure a facade package based on individual enhancements to resilience attributes.

Background: A lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch). A wellness score intuitively shows the users of smart wellness services the overall condition of their health behaviors. LWI development includes estimation (ie, estimating coefficients in LWI with data). A panel data set comprising health behavior lifelogs allows LWI estimation to control for unobserved variables, thereby resulting in less bias. However, these data sets typically have missing data due to events that occur in daily life (eg, smart devices stop collecting data when batteries are depleted), which can introduce biases into LWI coefficients. Thus, the appropriate choice of method to handle missing data is important for reducing biases in LWI estimations with panel data. However, there is a lack of research in this area. Objective: This study aims to identify a suitable missing-data handling method for LWI estimation with panel data. Methods: Listwise deletion, mean imputation, expectation maximization-based multiple imputation, predictive-mean matching-based multiple imputation, k-nearest neighbors-based imputation, and low-rank approximation-based imputation were comparatively evaluated by simulating an existing case of LWI development. A panel data set comprising health behavior lifelogs of 41 college students over 4 weeks was transformed into a reference data set without any missing data. Then, 200 simulated data sets were generated by randomly introducing missing data at proportions from 1% to 80%. The missing-data handling methods were each applied to transform the simulated data sets into complete data sets, and coefficients in a linear LWI were estimated for each complete data set. For each proportion for each method, a bias measure was calculated by comparing the estimated coefficient values with values estimated from the reference data set. Results: Methods performed differently depending on the proportion of missing data. For 1% to 30% proportions, low-rank approximation-based imputation, predictive-mean matching-based multiple imputation, and expectation maximization-based multiple imputation were superior. For 31% to 60% proportions, low-rank approximation-based imputation and predictive-mean matching-based multiple imputation performed best. For over 60% proportions, only low-rank approximation-based imputation performed acceptably. Conclusions: Low-rank approximation-based imputation was the best of the 6 data-handling methods regardless of the proportion of missing data. This superiority is generalizable to other panel data sets comprising health behavior lifelogs given their verified low-rank nature, for which low-rank approximation-based imputation is known to perform effectively. This result will guide missing-data handling in reducing coefficient biases in new development cases of linear LWIs with panel data.

@en