53 research outputs found
Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network
No full textThe latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs
Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network
No full textThe latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs
The Relation between Treated Maternal Urinary Tract Infection and Adverse Maternal, Prenatal Outcomes in Pregnant Women of Ardabil, Iran
No full textBackground and Objective: Urinary tract infection is one of the most common bacterial infections during pregnancy and has also been implicated as a risk factor for adverse maternal and prenatal outcomes. The aim of our study was to determine the relation between maternal urinary tract infection and adverse maternal, prenatal outcomes in pregnant women of Ardabil, Iran.
Material and Methods: This retrospective-case-control study was conducted on prenatal file records of pregnant women in Ardabil (2011). The pregnant women who had a positive urine culture in their prenatal files (N= 211) were considered as a case group and 232 ones without urinary tract infection as a control. Using a research- made questionnaire, the data related to present pregnancy and prenatal information was collected and analyzed by KrusKal Wallis, Chi- Square and Fisher statistical tests.
Results: Maternal age of under 25 (%61.6 vs. 56.5), body mass index of more than 30 (%18.3 vs. 15.6), primigravida (%55 vs. 48.8), hypertension (%2.4 vs. 1.3), hyperemesis Gravidarum (%14.8 vs. 12.6), frequency and dysuria (%1.9 vs. 0.9), low birth weight (%95.4 vs. 93.2), congenital malformation (%3.5 vs. 1.8), artificial milk feeding (%6.5 vs. 2.7), neonatal death (%0.9 vs. 0.0) are higher in urinary infection group, however the differences are not statistically significant. Other maternal and prenatal adverse outcomes such as diabetes, pre-eclampsia , hemoglobin level, prematurity, abortion and stillbirth have not significant relation with urinary infection.
Conclusion: Because of low level of adverse maternal or prenatal outcomes reported in our study, we conclude that screening and treatment of urinary tract infection in Ardabil health service is appropriate; therefore, no change is needed for present screening or treatment processes
Disability in Ohio : Current and Future Demand for Services
No full textIn an effort to project the size of Ohio s Medicaid long-term care expenditures between now and the year 2020, this report first projected the size of the population with severe physical and/or cognitive, intellectual and/or developmental, and mental illness disabilities. Then, assuming different rates of medical and long-term care inflations the author projected both the total Medicaid and Medicaid long-term care expenditures
Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
No full textThe seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficienc
A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
No full textAlthough averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings
Disability in Ohio : Current and Future Demand for Services
No full textIn an effort to project the size of Ohio s Medicaid long-term care expenditures between now and the year 2020, this report first projected the size of the population with severe physical and/or cognitive, intellectual and/or developmental, and mental illness disabilities. Then, assuming different rates of medical and long-term care inflations the author projected both the total Medicaid and Medicaid long-term care expenditures
Law of Desire: Temporary Marriage in Shi’i Iran
No full textLaw of Desire: Temporary Marriage in Shi’i Iran written by Shahla Haeri is a valuable academic contribution about Iran’s ethnography in general, temporary marriage in particular. The book examines the institution of the Shīʿī form of temporary marriage (mutʿah) known as sigheh amongst Iranians. The central focus of this study is to discover the underlying logic of the marriage contract and its implications for gender relations in society that offers models for male-female interpersonal relationships and the form of gender’s dialectical worldview toward themselves and others (p.x). Shahla Haeri gives the legal interpretations of mutʿah made by religious authorities as an introduction to the topic in order to explain how the concept of temporary marriage differs from permanent (nikāḥ) marriage and modern forms of prostitution. The author, through the book, strongly condemns the male manipulation of the institution of temporary marriage under the justification of personal wishes and desires that disregards the woman’s social dilemmas and individual rights. The initial ignorance of sigheh women about the reciprocal rights and obligations of the spouses within mutʿah marriages is highlighted as an important reason that induces women to engage in this misunderstood marriage type with the assumption of a similarity between permanent and temporary marriage
Law of Desire: Temporary Marriage in Shi’i Iran
No full textLaw of Desire: Temporary Marriage in Shi’i Iran written by Shahla Haeri is a valuable academic contribution about Iran’s ethnography in general, temporary marriage in particular. The book examines the institution of the Shīʿī form of temporary marriage (mutʿah) known as sigheh amongst Iranians. The central focus of this study is to discover the underlying logic of the marriage contract and its implications for gender relations in society that offers models for male-female interpersonal relationships and the form of gender’s dialectical worldview toward themselves and others (p.x). Shahla Haeri gives the legal interpretations of mutʿah made by religious authorities as an introduction to the topic in order to explain how the concept of temporary marriage differs from permanent (nikāḥ) marriage and modern forms of prostitution. The author, through the book, strongly condemns the male manipulation of the institution of temporary marriage under the justification of personal wishes and desires that disregards the woman’s social dilemmas and individual rights. The initial ignorance of sigheh women about the reciprocal rights and obligations of the spouses within mutʿah marriages is highlighted as an important reason that induces women to engage in this misunderstood marriage type with the assumption of a similarity between permanent and temporary marriage
Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures
No full textSeismic fragility assessment provides a substantial tool for assessing the seismic resilience of these buildings. However, using traditional numerical methods to derive fragility curves poses significant challenges. These methods often overlook the diverse range of buildings found in different regions, as they rely on standardized assumptions and parameters. Consequently, they may not accurately capture the seismic response of various building types. Alternatively, extensive data collection becomes essential to address this knowledge gap by understanding local construction techniques and identifying the relevant parameters. This data is crucial for developing reliable analytical approaches that can accurately derive fragility curves. To overcome these challenges, this research employs four Machine Learning (ML) techniques, namely Support Vector Regression (SVR), Stochastic Gradient Descent (SGD), Random Forest (RF), and Linear Regression (LR), to derive fragility curves for probability of collapse in terms of Peak Ground Acceleration (PGA). To achieve the research objective, a comprehensive input/output dataset consisting of on-site data collected from 646 masonry walls in Malawi is used. Adopted ML models are trained and tested using the entire dataset and then again using only the most highly correlated features. The study includes a comparative analysis of the efficiency and accuracy of each ML approach and the influence of the data used in the analyses. Random Forest (RF) technique emerges as the most efficient ML approach for deriving fragility curves for the surveyed dataset in terms of achieved lowest values for evaluation metrics of the ML methods. This technique scored the lowest Mean Absolute Percentage Error (MAPE) of 16.8 %, and the lowest Root Mean Square Error (RMSE) of 0.0547. These results highlight the potential of ML techniques, particularly RF, in derivation of fragility curves with proper levels of accuracy
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