Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

The 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

Flow Patterns During Convective Boiling in Microchannels

To develop a flow regime map for convective boiling in microchannels and to propose flow pattern-based models to predict the corresponding heat transfer coefficients, a thorough understanding of the existing flow patterns and their transitions is necessary. In the present study, high-speed photography is employed to observe the flow patterns in flow boiling of a dielectric liquid, FC-77, in parallel silicon microchannels of depth 400 μm and widths ranging from 100 to 5850 μm. In each test, liquid mass flux and inlet subcooling are fixed at 250 kg/m2s and 5°C, respectively, while the heat flux to the bottom of the heat sink is increased form zero to a value near the critical heat flux. Temperature and pressure are measured at several locations. A high-speed digital video camera is used to observe boiling patterns at frame rates ranging from 2000 to 24000 frames per second (fps). The images presented show a top view of the horizontal microchannels, at a location along the heat sink centerline and near the flow exit

Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning

Earthquake safety assessment of buildings through rapid visual screening

Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively

AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data

A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures

Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared

Pharmacogenetic Study on the Effect of Rivastigmine on PS2 and APOE Genes in Iranian Alzheimer Patients

Background/Aims: Alzheimer disease (AD) is a complex and genetically heterogeneous disorder, and certain genes such as PS2 and APOE4 contribute to the development of AD. Due to its heterogeneity, AD-predisposing genes could vary in different populations. Moreover, not all AD patients will respond to the same therapy. We specifically investigated the effect ofrivastigmine (Exelon) on PS2 and APOE genes in Iranian AD patients. Methods: A total of 100 AD patients, 67 patients with sporadic AD (SAD) and 33 patients with familial AD (FAD), receiving rivastigmine therapy and 100 healthy controls were studied.PCR-RFLP was used for genotyping of PS2 and APOE. Results: We found a positive association between the PS2 –A allele and SAD patients (pc = 0.01), and the PS2 +A/–A genotype was significantly more frequent in SAD than FAD patients (pc = 0.009). The APOE4 allele was associated with total AD, SAD and FAD (pc = 0.000002). Patients with the PS2 +A/–A genotype and bigenic genotypes of +A/–A·Ε3/Ε3 and +A/–A·Ε3/Ε4 were the best responders to Exelon therapy, and those with the PS2 +A/+A and APOE Ε3/Ε4 genotypes were the worst responders. Conclusion: Our findings suggest that the PS2 and APOE4 alleles and genotypes affect both AD risk and response to rivastigmine therapy

Diabetes mellitus and bell's palsy in Iranian population

During last decades many researchers have focused on the conditions associated with Bell's palsy including diabetes mellitus, hypertension, and viral infections. This study was performed to evaluate correlation of diabetes mellitus and Bell's palsy and some relevant features not discussed in the literature in an Iranian population. The presence of diabetes mellitus was evaluated in a total number of 275 subjects (75 patients with Bell's palsy and 200 control subjects). Diabetes mellitus was noted in 10 (13.3) patients with Bell's palsy among which 6 case were diagnosed as new cases of diabetes. Previous history of Bell's palsy was present in 10.67 of the subjects with Bell's palsy. This study confirms the correlation of diabetes mellitus and Bell's palsy for the first time in an Iranian population. We suggest screening tests for diabetes mellitus to be a routine part in the management of patients with Bell's palsy, especially in developing countries. © 2008 Tehran University of Medical Sciences. All rights reserved

ACCIÓN DE PLEUROTUS OSTREATUS (JACQ. EX FR) KUMM EN LA REMOCIÓN DEL COLORANTE AZUL TURQUESA EN BIORREACTORES AIR LIFT

Los hongos de pudrición blanca, constituyen una alternativa biotecnológica para la restitución de ecosistemas contaminados. El objetivo fue evaluar la acción de Pleurotus ostreatus (Jacq. ex Fr) Kumm en la remoción del colorante azul turquesa en birreactores air lift. Se aislaron el micelio de nueve hongos de pudrición blanca a partir de fragmentos del cuerpo fructífero colectados de áreas boscosas en Tingo -1 María, éstos fueron transferidos en caldo Sabouraud conteniendo 100, 200 y 400 mg·L de colorante azul turquesa como contaminante inducido para seleccionar al que muestre mejor desarrollo micelial durante 14 días a temperatura ambiente. El hongo seleccionado (P. ostreatus), fue reproducido en 80 mL de caldo Sabouraud y repicado en los biorreactores air lift conteniendo 800 mLde Medio Mínimo de Sales con 100, -1 200 y 400 mg·L de colorante azul turquesa. Se determinaron a 9 especies de hongos, distribuidas en 9 géneros y 5 familias, siendo en su mayoría Polyporaceae (4 géneros) y Coriolaceae (2 géneros). La remoción del colorante azul turquesa en biorreactores air lift por el hongo seleccionado P. ostreatuslogra una eficiencia de remoción significativa a los 20 días de 76,93, 69,25 y 84,76% a concentraciones de 100, -1 200 y 400 mg·L respectivamente, notándose claramente mayor remoción en los primeros 5 días y reducida en los 15 días siguientes. Así mismo, como parámetros medidos en los biorreactores se obtuvieron mayor consumo de oxígeno disuelto en los diez primeros días; incremento del dióxido de carbono después de los cinco días y un mínimo descenso de la temperatura interna y del pH. Esto demuestra que P. ostreatus, tienen una marcada acción de remoción de éste colorante

Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures

Seismic 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