Building Identification Wizard : Version 1.5.1

A Framework for Plans Permeable Breakwater Eco-Friendly Building Identification and Characteristics Materials Construction Study Case at Demak Village

Advances in Science Technology and Engineering Systems Journal ◽

10.25046/aj050230 ◽

2020 ◽

Vol 5 (2) ◽

pp. 235-240

Author(s):

Denny Nugroho Sugianto ◽

Sugeng Widada ◽

Anindya Wirastriya ◽

Aris Ismanto ◽

Retno Hartati ◽

...

Keyword(s):

Study Case ◽

Building Identification

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Village Building Identification Based on Ensemble Convolutional Neural Networks

Sensors ◽

10.3390/s17112487 ◽

2017 ◽

Vol 17 (11) ◽

pp. 2487 ◽

Cited By ~ 19

Author(s):

Zhiling Guo ◽

Qi Chen ◽

Guangming Wu ◽

Yongwei Xu ◽

Ryosuke Shibasaki ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Building Identification

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Sensitivity Coefficients Utilized in Nonlinear Estimation With Small Parameters in a Heat Transfer Problem

Journal of Basic Engineering ◽

10.1115/1.3424973 ◽

1970 ◽

Vol 92 (2) ◽

pp. 215-221 ◽

Cited By ~ 8

Author(s):

J. V. Beck

Keyword(s):

Model Building ◽

Transfer Problem ◽

Nonlinear Estimation ◽

Transient Heat Conduction ◽

Computer Time ◽

Sensitivity Coefficients ◽

Yield Information ◽

Small Parameters ◽

The Difference ◽

Building Identification

The method of nonlinear estimation for determining parameters in differential equations is extended to treat more efficiently the problem of determining dominant and small parameters. Using the sensitivity coefficients, it is shown how to determine the dominant parameters first using nonlinear estimation and then using linear least squares to find the small parameters. This procedure can save a considerable amount of computer time. Even more important is the application to model-building (identification). The residuals for the difference of the temperatures calculated, assuming the small parameters are zero, and the measured temperatures are shown to yield information for discriminating between alternate mathematical models. A transient heat-conduction example is given to illustrate some of the concepts developed.

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PHYSICAL AND SOCIAL VULNERABILITY IN THE HOUSHOLD LEVEL TOWARDS TSUNAMI IN SADENG COASTAL AREA, GUNUNGKIDUL

JURNAL GEOGRAFI ◽

10.24114/jg.v13i1.19160 ◽

2021 ◽

Vol 13 (1) ◽

pp. 59

Author(s):

Fitria Nucifera ◽

Sutanto Trijuni Putro ◽

Sakinatul Afidah

Keyword(s):

Coastal Area ◽

Social Vulnerability ◽

Tsunami Hazard ◽

Matrix Analysis ◽

Dependency Ratio ◽

Physical Vulnerability ◽

Household Level ◽

High Dependency ◽

Economic Assets ◽

Building Identification

Tsunami occurrence in Indonesia has continued to increase until 2018. The southern coast of Java is one of the tsunami-prone areas because it is located in a subduction zone. Study location is Sadeng coastal area which is located in the south coast of DIY Province. Disaster vulnerability studies at the household level is still limited, so this paper aims to identify physical and social vulnerability to tsunami hazard at the household level. The data of this research was obtained by invterviewing household respondents and observing physical condition of building. Identification of physical vulnerability was performed using modified SCHEMA and PTVA-3 method, while social vulnerability assessment considered demographic and socio-economic parameters. Total vulnerability was retrieved from matrix analysis of physical and social vulnerability classification. The study shows that 64 % households in Sadeng coastal areas are classified to moderate vulnerability, 30% of households are high vulnerability and 6 % of households are low vulnerability. High vulnerability is characterized by households which occupy non-permanent houses, have no economic assets, and have a high dependency ratio. Moderate vulnerability is characterized by households which occupy semi-permanent house, have economic assets, but have high dependency ratio. Low vulnerability is characterized by households which live in government-owned buildings, have economic assets, and have low dependency ratio. Keywords: tsunami, vulnerability, building`s physical vulnerability, social vulnerabilityKejadian tsunami di Indonesia terus mengalami peningkatan hingga tahun 2018. Pesisir selatan Jawa merupakan salah satu kawasan yang terpapar bahaya tsunami karena terletak pada zona subduksi. Lokasi kajian adalah kawasan pesisir Sadeng yang berlokasi di pesisir selatan Propinsi DIY. Kajian kerentanan bencana di tingkat rumah tangga belum banyak dilakukan, sehingga tulisan ini bertujuan untuk melakukan identifikasi kerentanan fisik bangunan dan sosial terhadap bencana tsunami di tingkat rumah tangga. Perolehan data penelitian dilakukan dengan wawancara responden rumahtangga dan observasi kondisi fisik bangunan. Identifikasi kerentanan fisik bangunan dilakukan dengan metode SCHEMA dan PTVA-3 yang dimodifikasi, sedangkan penilaian kerentanan sosial mempertimbangkan parameter kependudukan dan sosial ekonomi. Nilai total kerentanan diperoleh dari analisis matriks klasifikasi kerentanan sosial dan fisik bangunan. Kajian menunjukkan bahwa sebesar 64 % rumahtangga di kawasan pesisir Sadeng termasuk dalam kelas kerentanan sedang, 30 % rumahtangga dalam kerentanan tinggi dan 6 % rumahtangga dalam kerentanan rendah. Tingkat kerentanan tinggi dicirikan dengan rumahtangga yang menempati rumah tinggal non-permanen, tidak memiliki asset ekonomi, dan memiliki angka ketergantungan yang tinggi. Tingkat kerentanan sedang dicirikan dengan rumahtangga yang menempati rumah tinggal semi permanen, memiliki asset ekonomi namun memiliki angka ketergantungan yang tinggi. Tingkat kerentanan rendah dicirikan dengan rumahtangga yang tinggal pada bangunan milik pemerintah, memiliki asset ekonomi, dan memiliki angka ketergantungan yang rendah. Kata kunci: tsunami, kerentanan, kerentanan fisik bangunan, kerentanan sosial

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