Determinants of full and partial household evacuation decision making in hurricane Matthew
Authors : Roaa Alawadi, Pamela Murray-Tuite, David Marasco, Satish Ukkusuri, Yue G
Abstract : This paper adds partial household evacuation to the traditional binary evacuate/stay decision. Based on data from a survey of Jacksonville, FL residents after Hurricane Matthew, multinomial (MNL) and random parameter MNL models were developed to determine the influential factors and whether some variables’ effects are more nuanced than prior literature suggests. The random parameter model was preferred to the fixed parameters model. Variables significant in this model included injury concern, certainty about hurricane impact location, age, marital status, family cohesion, and living in mobile or detached homes. Greater injury concern results in lower likelihood of none of the household evacuating and greater likelihood of partial evacuation, but lower likelihood of full household evacuation. Similarly, greater certainty about hurricane impact increased the probability of partial household evacuation but decreased the probability of full evacuation. Respondent age had heterogenous effects; for 85.54% of respondents, additional years of age increased the likelihood of the household staying. Married households had a higher likelihood of staying or evacuating together. Similarly, greater family cohesion was associated with the household remaining together. Living in mobile homes decreased the likelihood that all of the household stays or evacuates and increased the probability of partial household evacuation. Living in a single-family detached home was associated with lower likelihood of all of the household staying or evacuating and a greater likelihood of a partial household evacuation. These findings can inform strategies that influence full or partial household evacuations, material requirements based on these decisions, and ways to reduce family risk.
Keywords : Hurricane evacuation; Partial Household evacuation; Random parameters; Hurricane Matthew
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Innovative and reliable model for shear strength of steel fibers reinforced concrete beams
Authors : A Tarawneh, G Almasabha, R Alawadi, M Tarawneh
Abstract : Literature includes several empirical models to predict shear capacity of steel fiber-reinforced concrete (SFRC) beams. However, some of these models were calibrated based on small database of experiments or include data for beams failed in flexure. This study utilizes a database of 241 tests of SFRC beams failed in shear along with gene expression programming (GEP) to establish more accurate and reliable predictions. Independent variables included in the GEP were fiber factor, longitudinal reinforcement ratio, concrete cylinder compressive strength, and shear span-to-depth ratio. The model provides an average ratio (vtest/vpredicted) of 1.00, a coefficient of variation of 24%, and a root mean square error of 1.33 MPa. The proposed model provides a higher accuracy when compared to existing models in the literature. The study also presents a reliability analysis study to evaluate the safety level embedded in the proposed model. By comparing the safety level of the proposed model to the safety level in reinforced concrete beams without shear reinforcement according to ACI 318-19 equation, a strength reduction factor of 0.5 instead of 0.75 is proposed to achieve consistent safety level with conventional reinforced concrete. Additionally, a varying strength reduction factor with respect to shear span-to-depth is also proposed.
Keywords : Steel fibers; Shear stress; Gene expression; Reliability analysis; Strength reduction factor
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Leveraging artificial intelligence for more accurate and reliable predictions of anchors shear breakout capacity in thin concrete members
Authors : Ahmad Tarawneh, Yazan Momani, Roaa Alawadi
Abstract : Understanding the shear behavior of post-installed anchors embedded in thin concrete members is considered complicated due to the many parameters involved including embedment depth, edge distance, concrete strength, anchor diameter, and member thickness. This study utilizes the Artificial Neural Network (ANN) concept to predict the capacity and behavior of adhesive and screw anchors with full-thickness embedment in thin concrete members and compare it with the adopted design model in ACI 318 standard. First, A multilayered feed-forward ANN model trained with Bayesian Regularization training algorithm is developed and compared against the proposed model based on the concrete capacity design method (CCD). Then, a parametric study is presented to evaluate the contribution of each variable to the shear capacity and ensure generalization. The study shows that ANN model provides a higher level of accuracy in predicting the shear capacity than CCD model by reducing the coefficient of variation from 18% to 11%. In addition, the parametric study shows that the CCD model does not capture the nonlinear effect of the variables on the capacity accurately and does not include the interaction effect between variables. For instance, CCD shows that the member thickness has a nearly linear effect on the capacity while ANN shows higher nonlinearity. In addition, the study presents a reliability analysis to assess the shift in safety level by adopting the ANN model. The ANN provided an average increase in reliability index by 32%. This study is considered the first study to utilize ANN to anchors embedded in thin concrete members with full embedment depth.
Keywords :Post-installed anchors; Screw anchors; Shear capacity; ANN; Reliability analysis
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Determinants of Departure Timing for Hurricane Matthew and Anticipated Consistency in Future Evacuation Departures
Authors : Roaa Alawadi, Pamela Murray-Tuite, Ruijie Bian
Abstract : This work investigated the factors affecting household choice of departure time during evacuations in Hurricane Matthew in 2016. Departure time estimates are needed to predict time-varying evacuation demand for use in simulation models and the development of evacuation traffic management strategies. The research team conducted a household survey after Hurricane Matthew in the Jacksonville, Florida, metropolitan area, with a total sample size of 588 respondents. Newly introduced factors were examined for significance throughout this work and were found to affect evacuation departure timing, such as uncertainty, family relationships, and cohesion. Uncertainty affects how certain the potential evacuees are about hurricane information such as hurricane impact location, whether they live in an evacuation zone, the timing of the hurricane and the evacuation destination and the route by which to get there, as well as the time needed to prepare for the evacuation. Family cohesion is related to decision-making agreement among household members and their preference to stay together in difficult situations. Such factors were poorly presented in previous literature. A Cox proportional-hazards model, a survival analysis technique used to study time till event, was used to model the evacuation departure timing, based on data from a post-Hurricane Matthew survey of Jacksonville, Florida, metropolitan area residents. The final model contained three significant variables, of which two are related to uncertainty and family cohesion. This study also used a binary logit model to examine evacuees’ retrospective preferences about whether they would have changed their evacuation timing. The preferred model contained five significant variables related to past experience, the type of evacuation order received, and the evacuation destination. This work opens several opportunities for additional studies on topics such as the stability of departure time; in addition, new factors presented here should be considered in future studies, such as certainty levels and household cohesion. Additional measures of experience could be incorporated to better understand the nuances of the experiences of different components of evacuation behavior.
Keywords : Hurricane evacuation; Departure timing; Cox proportional model; Hurricane Matthew; Uncertainty.
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Shear strength prediction of steel fiber-reinforced concrete beams without stirrups
Authors : Yazan Momani, Ahmad Tarawneh, Roaa Alawadi, Zaid Momani
Abstract : Understanding the shear strength behavior of steel fiber-reinforced concrete (SFRC) is considered complex due to various parameters involved, including steel fiber content, shear-span-to-depth ratio (a/d), reinforcement ratio (ρ), effective depth (d), and concrete compressive strength. This study utilizes artificial neural network (ANN) and gene expression programming along with a worldwide experimental database to develop prediction models for the shear strength of SFRC beams. In this study, a new proposed empirical equation to determine the shear strength of SFRC beams is presented and compared with other existing equations from literature. The study presents a parametric analysis using ANN to investigate and quantify the individual effect of each of the variables on shear strength. The proposed models were compared with other existing models from the literature based on the average shear values (tested/predicted), the root-mean-squared error (RMSE), and the coefficient of variation (COV). The equation proposed herein proved to provide the most accurate estimates of shear strength of SFRC based on the ratio of the measured strength to the calculated strength of 1.00 and a COV of 27%. It was noted from the parametric analysis that increasing the steel fiber factor (F) from 0 up to 1% will increase the shear capacity; however, increasing it by more than 1% will not attain additional shear strength to the SFRC beams. The parametric analysis also indicated that shear-span-to-depth ratio and reinforcement ratio contribute the most to shear strength
Keywords : Steel fber-reinforced concrete beam · Shear strength equation · ANN · GEP
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