Abstract. The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1), Immediate Occupancy (2), Life Safety (3) or in a condition of Collapse Prevention (4). According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network
The active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be difficult and time-consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-story building with the fixed floor plan using Backpropagation Neural Network (BPNN) method. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 datasets were obtained and fed into the BPNN model for the learning process. The trained BPNN is capable of predicting the displacement, velocity, and acceleration responses with up to 96% of the expected rate.
Meningkatnya kasus kerusakan jembatan di Indonesia mengartikanbahwasanya struktur jembatan di Indonesia masih banyak yang belum mampuberfungsi secara optimal. Hal ini juga berkaitan dengan pengaruh Indonesiayang terletak pada jalur tektonik dan vulkanik aktif yang menyebabkankerusakan struktur jembatan. Studi kasus untuk analisis ini menggunakanJembatan Sungai Siak 2 dengan bentang 200 m dengan jumlah pilar sebanyak2 buah. Pemilihan analisis ini dilakukan pada bagian pilar jembatan karenaberdasarkan fakta bahwa kegagalan strukur pada pilar jembatan akanmengakibatkan kegagalan sruktur jembatan secara keseluruhan. Analisisrespons struktur jembatan beton prategang box girder bertujuan untukmengidentifikasi besar nilai perpindahan struktur pada pilar jembatan terhadapbeban gempa rencana. Sementara itu, analisis numerik respons strukturjembatan menggunakan perangkat lunak Midas Civil V2.2. Analisis responsstruktur jembatan beton prategang box girder dilakukan berdasarkan tigakondisi tanah, yaitu tanah keras, tanah sedang, dan tanah lunak. Pada hasilpenelitian ini didapatkan nilai perpindahan pada pilar jembatan arahlongitudinal untuk kondisi tanah keras sebesar 0,88 mm, tanah sedang sebesar1,06 mm dan tanah lunak sebesar 1,39 mm. Dengan demikian dapatdisimpulkan bahwa nilai perpindahan jembatan akibat beban gempa rencanapada kondisi tanah keras lebih kecil 21,4% dibanding pada kondisi tanahsedang dan lebih kecil 60,32% dibanding tanah lunak. Maka dari itu, hasilpenelitian ini dapat dijadikan sebagai masukan kepada pihak pemerintah danpihak terkait yang bergerak di bidang konstruksi jembatan, khususnya tipejembatan box girder beton prategang di berbagai wilayah Indonesia, denganmemperhatikan respons struktur jembatan yang terjadi sesuai tingkat risikokegempaan untuk setiap daerah.
A technique to evaluate the potential progressive collapse of reinforced concrete structure was conducted in this study. The analysis involved the removal of several columns on critical location of the building according to General Services Administration (GSA) 2013 provision. In each analysis, the demand-capacity ratios (DCRs) of structural elements were examined and compared to the defined acceptance criteria. To avoid structural building collapse progressively, DCR ratio of regular and irregular buildings should be less than 2 and 1.5, respectively. The result showed that the structure did not collapse with the removal single column only. Further to this finding, several columns need to be removed so that it collapsed progressively. In the case of regular structure, progressive collapse occurred after removing five columns on the side of the regular structure, with the maximum DCR of 4.66. In the case of irregular structure, progressive collapse occurred after removing four columns on the horizontal side in the middle of structure with the maximum DCR of 3.44.
Kuat tekan dan kuat tarik belah merupakan parameter kekuatan beton yang penting. Penelitian ini dilakukan untuk mengkaji pengaruh penggunaan Natrium Tripolyphosphate (Na5P3O10) terhadap sifat mekanik beton cor di dalam air Under-Water Concrete (UWC). Digunakan variasi Na5P3O10 sebesar 5%, 10% dan 15% terhadap berat semen, serta penambahan filler berupa abu batu dengan presentase 10% terhadap berat agregat halus. Nilai kuat tarik belah dan kuat tekan beton diperoleh berdasarkan pengujian laboratorium dengan beton silinder diameter 10cm dan tinggi 20cm. Hasil penelitian menunjukkan kuat tekan dan kuat tarik belah beton paling rendah terjadi pada beton variasi Na5P3O10 5% yaitu 5.96MPa. Kuat tekan dan kuat tarik belah beton tertinggi yakni pada variasi Na5P3O10 10% sebesar 12.13MPa, namun terjadi penurunan pada beton variasi Na5P3O10 15% yakni 9.27Mpa. Hasil penelitian tersebut menunjukkan bahwa presentase Na5P3O10 5% menurunkan kuat tekan dan kuat tarik belah beton karena besarnya segregasi pada beton, sedangkan presentase Na5P3O10 15% justru mengurangi nilai workability beton segar sehingga tidak dapat memadat sendiri dengan baik. Penggunaan Na5P3O10 pada penelitian ini menunjukkan bahwa selanjutnya Na5P3O10 dapat digunakan sebagai Anti Washout Admixture pada Under-Water Concrete (UWC), namun memerlukan mix desain khusus Self Compacting
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