Modeling the effects of labor on housing reconstruction: A system perspective

Kumar, Sameer; Díaz, Rafael; Behr, Joshua G.; Toba, Ange-Lionel

doi:10.1016/j.ijdrr.2015.01.001

Cited by 20 publications

(10 citation statements)

References 20 publications

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“…Many quantitative studies have measured postdisaster housing recovery (or restoration) using improvement value data (Hamideh, 2015; Hamideh et al., 2018), permit data (Lester, Perry, & Moynihan, 2014; Stevenson, Emrich, Mitchell, & Cutter, 2010), or postdisaster aerial imagery of structures (Hoshi, Murao, Yoshino, Yamazaki, & Estrada, 2014) as proxy measures. Few probabilistic or predictive models exist for housing recovery including optimizing recovery outcomes from various temporary housing solutions (El‐Anwar, 2010; El‐Anwar, El‐Rayes, & Elnashai, 2010), a decision support system for assigning families to temporary housing units and locations (Rakes, Deane, Rees, & Fetter, 2014), an agent based model of household‐based decisions to rebuild (Nejat & Damnjanovic, 2012), a least absolute shrinkage and selection operator model on household decision making (Nejat & Ghosh, 2016), material resource system dynamics model on construction material supply (Diaz, Kumar, & Behr, 2015) and labor supply (Kumar, Diaz, Behr, & Toba, 2015) for rebuilding housing, and a Markov chain model for building functionality restoration that was designed generically, but could be applied to housing functionality restoration (Lin & Wang, 2017). Most of these studies focus on the physical process of rebuilding, and on recovery of houses, as opposed to recovery of households.…”

Section: Existing Recovery Modelsmentioning

confidence: 99%

Postdisaster Housing Stages: A Markov Chain Approach to Model Sequences and Duration Based on Social Vulnerability

Sutley

Hamideh

2020

Risk Analysis

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Housing recovery is an unequal and complex process presumed to occur in four stages: emergency shelter, temporary shelter, temporary housing, and permanent housing. This work questions the four-stage typology and examines how different types of shelter align with multiple housing recovery stages given different levels of social vulnerability. This article also presents a Markov chain model of the postdisaster housing recovery process that focuses on the experience of the household. The model predicts the sequence and timing of a household going through housing recovery, capturing households that end in either permanent housing or a fifth possible stage of failure. The probability of a household transitioning through the stages is computed using a transition probability matrix (TPM). The TPM is assembled using proposed transition probability models that vary with the social vulnerability of the household. Monte Carlo techniques are applied to demonstrate the range of sequences and timing that households experience going through the housing recovery process. A set of computational rules are established for sending a household to the fifth stage, representing a household languishing in unstable housing. This predictive model is exemplified on a virtual community, Centerville, where following a severe earthquake scenario, differences in housing recovery times exceed four years. The Centerville analysis results in nearly 5% of households languishing in unstable housing, thereby failing to reach housing recovery. These findings highlight the disparate trajectories experienced by households with different levels of social vulnerability. Recommendations are provided at the end for more equitable postdisaster recovery policies.

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Section: Existing Recovery Modelsmentioning

confidence: 99%

Postdisaster Housing Stages: A Markov Chain Approach to Model Sequences and Duration Based on Social Vulnerability

Sutley

Hamideh

2020

Risk Analysis

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“…A multilinear regression model developed by the author in [52] can be used for predicting approximate increases in cost, which helps in allocating sufficient funds in the planning phase. The simulation dynamics (SD) developed by author in [53] helps managers maker sounder decisions pertaining to whether to hire more laborers or operate with the currently available workforce.…”

Section: Identification Of Management Strategiesmentioning

confidence: 99%

Impact of Natural Disasters on Construction Projects: Strategies to Prevent Cost and Schedule Overruns in Reconstruction Projects

Pamidimukkala¹,

Kermanshachi²,

Sanjgna³

2020

Proceedings of the Creative Construction E-Conference 2020

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The number of natural catastrophic events has increased remarkably in recent decades, and the resulting challenges of construction projects have increased even more. It is imperative to recognize these challenges and employ suitable strategies to mitigate them to avoid project failures, which is the basis of this study. To become more knowledgeable about this subject, more than 100 scholarly articles, including peerreviewed papers and other types of publications, were reviewed and a list of eleven challenges was developed. The next step was to identify the management strategies that could be applied to overcome the challenges. The outcomes from this study concisely help decision-makers and project managers allocate their resources wisely after a disaster, implement construction activities more efficiently, and achieve higher rates of productivity while reducing the consequences of disruptive events.

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“…Training and education for a legacy, transitional and future workforce is a major opportunity for coordination and investment. Following disasters, there is often a tremendous shortage among general contractors and skilled and semi-skilled trades working in the construction industry (Kumar et al 2015). For instance, following Hurricane Irma in 2017, the State of Florida dedicated $20 million from the Community Development Block Grant Disaster Recovery allocations to a Workforce Recovery Training Program to train Florida residents in the construction trades (FDEO 2019).…”

Section: Potential Activities Under the Planning Presumption Clausementioning

confidence: 99%

Climate Adaptation Investment and the Community Reinvestment Act

Keenan¹,

Mattiuzzi²

2019

CDRB

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Modeling the effects of labor on housing reconstruction: A system perspective

Cited by 20 publications

References 20 publications

Postdisaster Housing Stages: A Markov Chain Approach to Model Sequences and Duration Based on Social Vulnerability

Postdisaster Housing Stages: A Markov Chain Approach to Model Sequences and Duration Based on Social Vulnerability

Impact of Natural Disasters on Construction Projects: Strategies to Prevent Cost and Schedule Overruns in Reconstruction Projects

Climate Adaptation Investment and the Community Reinvestment Act

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