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Perilaku Struktur Jembatan Baja Pelengkung Berdasarkan Spektrum Gempa

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Abstract

[ID] Perencanaan struktur jembatan baja pelengkung harus memperhatikan kemampuan respon strukturnya yang rentan terhadap deteriorasi akibat fatik, ancaman gempa bumi kuat atau angin topan, khususnya diwilayah sumatera yang mempunyai resiko gempa yang tinggi. Penelitian ini fokus memprediksi struktur jembatan pelengkung baja dengan analisis repons spectra dengan bantuan software analisis struktur gempa berdasarkan SNI 1726-2012. Percepatan gempa yang diambil berasal dari beberapa kota seperti Kota Aceh, Padang, Tanjung Pinang, dan Pekanbaru yang memliki karakteristik. Hasil analisis menunjukkan respon struktur jembatan terbesar terjadi di Padang dengan nilai perpindahan sebesar 0,016267 m dan percepatan sebesar 0,0235 m. Sementara itu, respons struktur terkecil terjadi di kota tanjung pinang dengan nilai perpindahan sebesar 0,01552 m dan nilai percepatan sebesar 0,0208 m. Diharapkan dengan diketahuinya hasil prediksi kesehatan struktur jembatan dapat digunakan sebagai referensi/masukan bagi pemerintah dan pihak yang terkait dalam usaha memperbaiki jembatan dengan tepat, sehingga diharapkan dapat mencegah terjadinya keruntuhan struktur jembatan. [EN] Curved steel bridge structure planning must pay attention to the responsiveness of the structure that is vulnerable to deterioration due to fatigue, the threat of strong earthquakes or hurricanes, especially in the region of Sumatra which has a high earthquake risk. This study focuses on predicting the structure of steel curved bridges with spectral response analysis with the help of earthquake structure analysis software based on SNI 1726-2012. The earthquake acceleration taken came from several cities such as Aceh City, Padang, Tanjung Pinang, and Pekanbaru which have characteristics. The analysis shows the largest bridge structure response occurred in Padang with a displacement value of 0.016267 and acceleration of 0.0235. Meanwhile, the smallest structural response occurred in Tanjung Pinang city with a displacement value of 0.01552 and an acceleration value of 0.0208. It is expected that by knowing the results of the bridge structure health predictions can be used as a reference / input for the government and related parties in an effort to repair the bridge appropriately, so that it is expected to prevent the collapse of the bridge structure.
http://journal.uir.ac.id/index.php/saintis
ISSN (Print) : 1410-7783
ISSN (Online) : 2580-7110
Volume 19 Nomor 02, Oktober 2019 : 71-78
71
Perilaku Struktur Jembatan Baja Pelengkung Berdasarkan
Spektrum Gempa
Behavior of Curved Steel Bridge Structures Based on Earthquake Spectrum
Widya Apriani1,*, Fadrizal Lubis1, Reni Suryanita2, Elva Nidya Sari1
1Jurusan Teknik Sipil, Fakultas Teknik Universitas Lancang Kuning, Jl. Yos Sudarso Km. 8 Pekanbaru, Riau
2Jurusan Teknik Sipil, Fakultas Teknik Universitas Riau, Jl. HR Soebrantas KM.12.5 Pekanbaru, Riau
* Penulis korespondensi : widyaapriani@unilak.ac.id
Tel.: +62-85271620554; fax.: -
Diterima: 14 Oktober 2019; Direvisi: 21 Oktober 2019; Disetujui: 23 Oktober 2019.
DOI: 10.25299/saintis.2019.vol19(02).3924
Abstrak
Perencanaan struktur jembatan baja pelengkung harus memperhatikan kemampuan respon strukturnya yang rentan terhadap
deteriorasi akibat fatik, ancaman gempa bumi kuat atau angin topan, khususnya diwilayah sumatera yang mempunyai resiko gempa
yang tinggi. Penelitian ini fokus memprediksi struktur jembatan pelengkung baja dengan analisis repons spectra dengan bantuan
software analisis struktur gempa berdasarkan SNI 1726-2012 m. Percepatan gempa yang diambil berasal dari beberapa kota seperti
Kota Aceh, Padang, Tanjung Pinang, dan Pekanbaru yang memliki karakteristik. Hasil analisis menunjukkan respon struktur jembatan
terbesar terjadi di Padang dengan nilai perpindahan sebesar 0,016267 m dan percepatan sebesar 0,0235 m. Sementara itu, respons
struktur terkecil terjadi di kota tanjung pinang dengan nilai perpindahan sebesar 0,01552 m dan nilai percepatan sebesar 0,0208 m.
Diharapkan dengan diketahuinya hasil prediksi kesehatan struktur jembatan dapat digunakan sebagai referensi/masukan bagi
pemerintah dan pihak yang terkait dalam usaha memperbaiki jembatan dengan tepat, sehingga diharapkan dapat mencegah terjadinya
keruntuhan struktur jembatan.
Kata Kunci: Jembatan baja pelengkung, respons struktur, spektra gempa sumatera.
Abstract
Curved steel bridge structure planning must pay attention to the responsiveness of the structure that is vulnerable to deterioration due
to fatigue, the threat of strong earthquakes or hurricanes, especially in the region of Sumatra which has a high earthquake risk. This study
focuses on predicting the structure of steel curved bridges with spectral response analysis with the help of earthquake structure analysis
software based on SNI 1726-2012. The earthquake acceleration taken came from several cities such as Aceh City, Padang, Tanjung Pinang,
and Pekanbaru which have characteristics. The analysis shows the largest bridge structure response occurred in Padang with a displacement
value of 0.016267 and acceleration of 0.0235. Meanwhile, the smallest structural response occurred in Tanjung Pinang city with a
displacement value of 0.01552 and an acceleration value of 0.0208. It is expected that by knowing the results of the bridge structure health
predictions can be used as a reference / input for the government and related parties in an effort to repair the bridge appropriately, so that it
is expected to prevent the collapse of the bridge structure.
Keywords: Curved steel bridge, structural response, Sumatra earthquakes spectra.
PENDAHULUAN
Perencanaan struktur jembatan harus
memperhatikan respon strukturnya terhadap
lingkungan seperti ancaman gempa bumi yang kuat,
atau angin topan karena struktur jembatan rentan
terhadap kerusakan dan deteriorasi selama masa
layan [1].
Jembatan baja pelengkung merupakan salah
satu tipe jembatan dengan tipe struktur rangka yang
membentuk kurva lengkung kemudian disatukan
dengan hanger/kabel baja sebagai penyalur beban
lantai jembatan menuju ke tumpuan jembatan[2].
Jembatan ini merupakan jembatan yang terdiri dari
berbagai jenis material penyusun seperti beton
bertulang, baja pelengkung, kayu.
Konstruksi Jembatan pelengkung memiliki
keunggulan antara lain bentuk jembatan
pelengkung merupakan inovasi dari peradaban
manusia yang memiliki nilai estetika tinggi dan
memiliki kekuatan yang tinggi terbukti jembatan
kuno Romawi menggunakan tipe ini dan masih
berdiri hingga kini. Namun konstruksi rangka baja
ini memiliki kelemahan khususnya pada biaya
pemeliharaan dan juga rentan terhadap resiko
yang dapat mengakibatkan strukturnya menekuk.
Bukan hanya itu, konsentrasi tegangan yang
terlalu tinggi juga bisa menjadikan baja kehilangan
daktilitasnya [3]. Oleh karena itu perlu adanya
monitoring struktur sebelum kegagalan bencana
terjadi, salah satunya yaitu dengan analisis respon
spectrum [4]. Tujuan penelitian ini difokuskan
J. Saintis Volume 19 Nomor 2, 2019
72
untuk menentukan respons spektrum jembatan
berupa nilai perpindahan struktur dan nilai
percepatan struktur akibat pengaruh lokasi dan
jenis tanah yang berbeda (3 jenis tanah: tanah
lunak, tanah sedang dan tanah keras). Dalam
menganalisis repsons spketrum, di ambil wilayah
sumatera yaitu kota padang, aceh, tanjung pinang
dan pekanbaru sebagai pembanding kekuatan
struktur jembatan tersebut.
Terdapat beberapa metode dalam
menganalisis beban gempa antara lain metode static
ekuivalen, metode dinamik respon sspektrum dan
metode analisis dinamik nonlinier time history [5].
Analisis dinamik memiliki keunggulan dibandingkan
dengan analisis static yang hanya terbatas oleh
bentuk konfigurasi ruang dan ketinggian bangunan.
Analisis resposn spektrum merupakan analisis
dinamik yang dapat menunjukkan kondisi respons
struktur menurut waktu puncak dari percepatan
gempa. Respons spektrum adalah grafik yang
menyatakan hubungan antara periode getar
struktur (T) dengan respons struktur maksimum
saat mengalami getaran gempa tertentu[6].
METODOLOGI
Respons spektrum merupakan sutu spektrum
hasil analisis dinamik beban gempa yang
mengambil titik-titik maksimum dari respons
gempa dalam hubungan periode dengan respons
sturktur yang dinyatakan dalam suatu grafik[7].
Prosedur untuk memperoleh grafik respons
spektrum gempa adalah sebagai berikut.
1. Menentukan nilai spectral percepatan pada 0,2
detik (Ss) dan spectral percepatan pada 1 detik
(S1).
2. Menetukan Koefisien Situs (Fa dan Fv)
Nilai Fa dan Fv ditentukan berdasarkan kelas
situs tanah yaitu SA (batuan keras), SB (Batuan),
SC (Tanah keras, sangat padat dan batuan
lunak), SD (tanah sedang), SE (Tanah lunak),
dan SF.
Menentukan Koefisien situs (Fa)
Tabel 1. Koefisien Situs Fa
Kelas
situs
Parameter Respons Spektral Percepatan
Gempa MCER terpetakan pada Periode
Pendek, T =0,2 detik, Ss
Ss ≤ 0,25
Ss= 0.5
Ss= 0.75
SA
0.8
0.8
0.8
SB
1.0
1
1
SC
1.2
1.2
1.1
SD
1.6
1.4
1.2
SE
2.5
1.7
1.2
Catatan : Untuk nilai-nilai antara Ss dilakukan
interpolasi linier
Menentukan koefisien situs (Fv)
Diketahui S1 = 0.3
Tabel 2. Koefisien Situs Fv
Kelas
situs
Parameter Respons Spektral Percepatan
Gempa MCER terpetakan pada Perioda 1
detik, S1
S1 ≤ 0.1
S1= 0.2
S1= 0.3
S1= 0.4
S1 ≥ 0.5
SA
0.8
0.8
0.8
0.8
0.8
SB
1.0
1
1
1.0
1.0
SC
1.7
1.6
1.5
1.4
1.3
SD
2.4
2
1.8
1.6
1.5
SE
3.5
3.2
2.8
2.4
2.4
Catatan : Untuk nilai-nilai antara Ss dilakukan
interpolasi linier
3. Menentukan Spektral Respons Percepatan
(Spectral Response Acceleration) SDS dan SD1
untuk
SMS = Ss × Fa (1)
SM1 = S1 × Fv (2)
SDS =
× SMS (3)
SD1 =
× SM1 (4)
4. Menghitung parameter respons spectrum
desain
 
 (5)
Ts = 
 (6)
Untuk periode yang lebih kecil dari ,
spectrum respons percepatan desain , Sa, harus
diambil dari persamaan[7] :
 󰇡 
󰇢 (6)
Untuk periode yang lebih besar dari Ts, Sa
berdasarkan persamaan [8]:

(7)
Diperoleh grafik respons spektrum:
Gambar 1. Respons Spekta Pekanbaru
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.00 2.00 4.00 6.00
Percepatan
waktu
Perilaku Struktur Jembatan Baja Pelengkung Berdasarkan Spektrum Gempa (Widya Apriani, dkk)
73
Gambar 2. Input Data Response Spectrum Gempa
Pekanbaru
Selanjutnya ahap analisis jembatan dilakukan
dengan menggunakan bantuan software struktur
elemen hingga. Adapaun respons yang akan ditinjau
adalah perpindahan dan percepatan struktu
jembatan[9]. Tahap awal pemodelan jembatan
dilakukan dengan mendefinisikan materal dan jenis
penampang setiap elemen jembatan.
Gambar 3. longitudinal cross section Jembatan
Pelengkung
Section properties jembatan yang digunakan
pada penelitian ini antara lain:
1. Tipe konstruksi = Baja pelengkung
2. Diameter hanger= 10 cm
3. Lebar jalan= 7 m
4. Lebar bahu=1,6 m
5. Lebar jembatan seluruhnya= 10.2 m
6. Rangka pelengkung= 120 X 80 cm t24 mm
7. Rangka bawah = 60 X 60 cm t 24 mm
8. Diafragma = 400x400 cm
9. Girder= 600x600 cm
10. Baja mutu = JIS G 3106 SM YB
11. Tegangan leleh (fy) = 295 Mpa
12. Tegangan putus/ultimate (fu) = 490 Mpa
13. Modulus elastisitas 200000 Mpa
14. Berat jenis= 78.5 kN/m3
15. Baja mutu Grade = 490 MPa
16. Tegangan leleh = 490 MPa
17. Tegangan putus = 610 MPa
18. Modulus elastis = 210000 MPa
19. Berat jenis = 78.5 kN/m3
Tahapan selanjutnya yaitu membuat
komponen gelagar, diafragma, dan perletakan
jembatan. Setelah itu dilakukan penginputan
beban jembatan seperti beban mati, beban lalu
lintas dan beban gempa berupa respons spectrum
untuk masing masing lokasi[10]. Perhitungan
pembebanan digunakan untuk menentukan beban
yang bekerja di jembatan. Beban yang dimasukkan
sesuai dengan standar SNI Pembebanan jembatan
tahun 2016[11][12]. Berat sendiri struktur
jembatan dihitung secara otomatis oleh SAP
2000[13]. Adapun beban berat sendiri yang
diperhitungkan antara lain:
1. Beban Tetap Berat sendiri, terdiri atas:
Berat sendiri baja = 7850 kg/m3
(78 kN/m3)
Berat sendiri beton (deck slab) = 2400
kg/m3 (24 kN/m3)
Kedua beban diatas didefinisikan langsung
oleh program SAP2000.
Berat sendiri trotoar (beton) = 2400
kg/m3 (24 kN/m3)
Berat aspal beton = 2240 kg/m3
(22 kN/m3)
a. lantai
luas lantai = 0.5(0.1 m +0.13 m)×11 m =
1.265 m2
total berat = 1.265 m2 × (98/20) ×24 kN/m3
= 148.764 kN
b. balok memanjang
total panjang balok = 4.9 m
luas balok hollow 400 x 600 = 78 kN/m3 x
0.2352 m3= 18.346 kN
total berat balok memanjang = 18.346 kN
2. Beban kerb dan railing
Beban trotoar dan lapisan aspal beton
merupakan beban mati sekunder/ tambahan
(super impose dead load) yang beratnya tetap
namun dapat berubah selama masa layan
jembatan[13][14], dan diperhitungkan
sebagai input beban dalam SAP2000.
Beban trotoar = 0.2 m x 24 kN/m3 = 4.8
kN/m2
Trotoar disepanjang bentang pada dua sisi
jembatan dengan lebar 1,5 meter dan bekerja
sebagai beban merata area.
Beban railing = 0.5 x 0.8 m x (0.25+
0.15) m x 24 kN/m3= 3.84 kN/m
Railing berada di sepanjang bentang pada
kedua sisi jembatan dan bekerja sebagai
beban garis merata[15].
Total Berat = 2 ( kanan dan kiri) x 3.84 kN/m
x 4.9 m = 37.632 kN
Sedangkan beban mati tambahan dihitung
dengan BTR = 9,0󰇡 
󰇢 5.34375 kN/m2
dan beban garis terpusat yang digunakan adalah
49 kN/m dengan faktor pembesaran dinamis
1,375. Berat truk uji dan penempatan beban truk
J. Saintis Volume 19 Nomor 2, 2019
74
uji disesuaikan dengan kondisi pada saat pengujian
beba[16][17][18].
Selanjutnya dilakukan analisis respons
spectrum dengan melihat ragam komulatif dengan
partisipasi lebih dari 90%[19][20]. Selanjunya di
analaisis nilai perpindahan dan percepatan untuk
arah x dana rah y[21].
HASIL DAN DISKUSI
Sesuai SNI 1726-2012 jumlah pola getar yang
ditinjau dalam penjumlahan respon ragam
mencakup partisipasi sekurang kurangnya
90%[22][23]. Ragam getar struktur yang diperoleh
digunakan untuk menentukan karakteristik dinamik
dari suatu system strktur. Ragam getar ini
didefinisikan oleh properti fisik serta distribusi
spasial dari komponen penyusun system
struktur[24]. Dari hasil diperoleh ragam pada mode
pertama hingga ketiga adalah linier dengan
partisipasi massa sebesar 90%.. Dalam analisis
dinamik yang dilakukan, digunakan 15 ragam pola
getar dan patisipasi massa yang disumbangkan
masing-masing 90.22% pada mode ke 8 untuk
translasi arah x (SUM UX), sebesar 92.79% pada
mode ke 14 untuk translasi arah y (SUM UY) dan
sebesar 93.55% pada mode ke 9 untuk rotasi arah
sumbu z (SUM RZ).
Berdasarakan gambar 4 diketahui perioda
fundamental T sebesar 1,61345 det dan frekuenis f
sebesar 0,61979 Hz. Titik pengamatan dilakukan
pada tengah jembatan (nodal 234) yang merupakan
titik maksimum displacementnya (Gambar 4).
Hasil analisis respon struktur berupa
perpindahan perpindahan dan percepatan untuk
tinjauan 4 lokasi diwilayah sumatera yang memiliki
kondisi gempa dan kondisi tanah yang berbeda-
beda. Titik tinjauan dapat dilihat pada gambar
berikut ini.
Gambar 4. Titik Pengamatan
Ragam ke-1 (T = 1,61345 det;f = 0,61979 Hz)
Ragam ke-2 (T = 1,59641 det;f = 0,62641 Hz)
Ragam ke-3 (T = 1,31245 det;f = 0,76193 Hz)
Ragam ke-4 (T = 1,09848 det;f = 0,91035 Hz)
Ragam ke-4 (T = 1,09848 det;f = 0,91035 Hz)
Ragam ke-5 (T = 0,81380 det;f = 1,22880 Hz)
Perilaku Struktur Jembatan Baja Pelengkung Berdasarkan Spektrum Gempa (Widya Apriani, dkk)
75
Gambar 5. Perpindahan Arah X Pada Titik
Pengamatan
Berdasarkan gambar 5 wilayah padang
memiliki nilai perpindahan tertinggi yaitu 0,019822
m dengan kondisi tanah sedang kemudian diikuti
dengan tanah lunak. Sementara nilai perpindahan
minimum yaitu wilayah tanjung pinang dengan nilai
perpindahan 0,017903 m dan kondisi tanah keras.
Untuk daerah kontur gempa tinggi yaitu aceh dan
padang secara umum memiliki respons
peripindahan yang lebih besar dibandingkan
dengan daerah tanjung pinang dan pekanbaru. Hal
ini mengindikasikan bahwa jenis tanah dan
percepatan tanah terhadap gempa menentukan
respons struktur pada jembatan tersebut.
Sedangkan perpindahan maksimum dan minimum
arah y dapat dilihat pada gambar 6 berikut ini.
Gambar 6. Perpindahan Arah Y Pada Titik
Pengamatan
Nilai perpindahan maksimum arah y terdapat
di wilayah padang dengan kondisi tanah sedang dan
nilai perpindahannya yaitu sebesar 0,016267 m.
Dan nilai perpindahan minimum arah y terdapat di
wilayah tanjung pinang dengan kondisi tanah keras
dan besar perpindahan yang terjadi yaitu sebesar
0,01552 m. Untuk daerah kontur gempa tinggi yaitu
aceh dan padang secara umum memiliki respons
peripindahan yang lebih besar dibandingkan
dengan daerah tanjung pinang dan pekanbaru. Hal
ini mengindikasikan bahwa jenis tanah dan
percepatan tanah terhadap gempa menentukan
respons struktur pada jembatan tersebut.
Percepatan pada arah x dan y dapat dilihat
pada gambar dibawah ini.
Gambar 7. Percepatan Arah X Pada Titik
Pengamatan
Berdasarkan gambar 7 nilai percepatan
arah x maksimum terjadi pada wilayah padang
dengan nilai percepatan sebesar 0,0235 m dan
kondisi tanah sedang. Sedangkan nilai percepatan
minimum arah x terdapat diwilayah tanjung
pinang dengan kondisi tanah keras. Dan nilai
percepatan nya sebesar 0,0012 m. Untuk daerah
kontur gempa tinggi yaitu aceh dan padang secara
umum memiliki respons peripindahan yang lebih
besar dibandingkan dengan daerah tanjung pinang
dan pekanbaru. Hal ini mengindikasikan bahwa
jenis tanah dan percepatan tanah terhadap gempa
menentukan respons struktur pada jembatan
tersebut.
Gambar 8. Percepatan Arah Y Pada Titik
Pengamatan
0.0165
0.017
0.0175
0.018
0.0185
0.019
0.0195
0.02
Perpindahan x(m)
Kota
tanah keras tanah sedang tanah lunak
0.015
0.0152
0.0154
0.0156
0.0158
0.016
0.0162
0.0164
Perpindahan y(m)
Kota
tanah keras tanah sedang tanah lunak
0
0.005
0.01
0.015
0.02
0.025
Percepatan x(m/s2)
Kota
tanah keras tanah sedang tanah lunak
0.019
0.0195
0.02
0.0205
0.021
0.0215
0.022
0.0225
0.023
0.0235
0.024
Percepatan y (m/s2)
Kota
tanah keras tanah sedang tanah lunak
J. Saintis Volume 19 Nomor 2, 2019
76
Pada arah Y pada titik pengamatan
menghasilkan nilai percepatan maksimum arah y
terdapat pada wilayah padang, kondisi tanah sedang
dengan nilai percepatan 0,0235 m. Dan nilai
percepatan minimum arah y yaitu wilayah tanjung
pinang dengan kondisi tanah keras dan tanah
sedang. Dan nilai percepatannya yaitu sebesar
0,0208 m. Sedangkan wilayah aceh dengan nilai
percepatan sama baik dengan kondisi tanah keras,
tanah sedang dan tanah lunak. Untuk daerah
konturrgempa tinggi yaitu aceh dan padang secara
umum memiliki respons peripindahan yang lebih
besar dibandingkan dengan daerah tanjung pinang
dan pekanbaru. Hal ini mengindikasikan bahwa
jenis tanah dan percepatan tanah terhadap gempa
menentukan respons struktur pada jembatan
tersebut.
KESIMPULAN
Kesimpulan dari penelitian ini antara lain
adalah: (a) nilai perpindahan dan percepatan
maksimum untuk suatu lokasi untuk daerah Aceh,
Padang, Tanjung Pinang dan Pekanbaru
diperngaruhi oleh jenis tanah pada lokasi tersebut.
Nilai Maksimum terjadi pada tanah sedang dan
tanah Lunak. Sedangkan nilai minimum terjadi pada
tanah keras; (b) respons Struktur perpindahan dan
percepatan terbesar terjadi di Padang yaitu untuk
nilai perpindahan sebesar 0,016267 m dan
percepatan sebesar 0,0235 m . Sedangkan untuk
nilai respons struktur terkecil; (c) respons struktur
terkecil terjadi di kota Tanjung Pinang yaitu
perpindahan sebesar 0,01552 m dan percepatannya
sebesar 0,0208 m.
UCAPAN TERIMA KASIH
Terima kasih kepada Kemenristek Dikti yang
telah mendukung melalui program hibah Penelitian
Kerja Sama Perguruan Tinggi Tahun 2019-2020.
Serta Prodi Teknik Sipil Universitas Lancang
Kuning.
REFERENSI
[1] T. BMS, “Bridge Management System,” p. 1,
1993.
[2] W. Apriani, S. W. Megasari, W. Alrisa, and P.
Loka, “Penilaian Jembatan Rangka Baja Transfield
Australia Dengan Metode Fracture Critical Member (
Studi Kasus : Jembatan Siak 2 Pekanbaru ),” no.
September, pp. 1819, 2018.
[3] R. Suryanita and A. Adnan, “Application of
Neural Networks in Bridge Health Prediction based
on Acceleration and Displacement Data Domain
Application of Neural Networks in Bridge Health
Prediction based on Acceleration and Displacement
Data Domain,” vol. I, no. February 2016, pp. 4–9,
2013.
[4] Mardiyono, R. Suryanita, and A. Adnan,
“Intelligent monitoring system on prediction of
building damage index using neural-network,”
TELKOMNIKA (Telecommunication Comput.
Electron. Control., vol. 10, no. 1, pp. 155164, 2012.
[5] R. Suryanita, “The Application of Artificial
Neural Networks in Predicting Structural
Response of Multistory Building in The Region of
Sumatra Island,” KnE Eng., vol. 1, no. 2015, pp. 16,
2016.
[6] R. Suryanita, H. Maizir, and H. Jingga,
“Prediction of Structural Response due to
Earthquake Load using Artificial Neural
Networks,” Int. Conf. Eng. Technol. Comput. Basic
Appl. Sci. ECBA, 2016, Osaka, Japan, vol. 182, no. 4,
2016.
[7] Mardiyono, R. Suryanita, and A. Adnan,
“Intelligent monitoring system on prediction of
building damage index using neural-network,”
TELKOMNIKA (Telecommunication Comput.
Electron. Control., vol. 10, no. 1, pp. 155164, 2012.
[8] J. Brownjohn, “Structural Health Monitoring
of the Tamar Bridge,” Vce.At, pp. 465490, 1961.
[9] J.-J. Lee and C.-B. Yun, “Damage localization
for bridges using probabilistic neural networks,”
KSCE J. Civ. Eng., vol. 11, no. 2, pp. 111120, 2008.
[10] S. Tohidi and Y. Sharifi, “A new predictive
model for restrained distortional buckling strength
of half-through bridge girders using artificial
neural network,” KSCE J. Civ. Eng., vol. 20, no. 4, pp.
13921403, 2016.
[11] P. H. a Nababan, “Structural Health
Monitoring System Alat Bantu Mempertahankan
Usia Teknis Jembatan,” Constr. Maint. main span
Suramadu Bridg., pp. 12, 2008.
[12] N. M. Apaydin, A. C. Zulfikar, and H. Alcik,
“Introduction of Bogazici Suspension Bridge
Structural Health Monitoring System,” 15th World
Conf. Earthq. Eng., 2012.
[13] M. Mehrjoo, N. Khaji, H. Moharrami, and A.
Bahreininejad, “Damage detection of truss bridge
joints using Artificial Neural Networks,” Expert
Syst. Appl., vol. 35, no. 3, pp. 11221131, 2008.
[14] Z. Chen, X. Zhou, X. Wang, L. Dong, and Y.
Qian, “Deployment of a smart structural health
monitoring system for long-span arch bridges: A
review and a case study,” Sensors (Switzerland),
vol. 17, no. 9, 2017.
[15] D. S. Shan, P. Yan, and Z. H. Wang,
“Intelligent Health Monitoring System for a
Railway Cable-Stayed Bridge,” Adv. Mater. Res., vol.
148149, no. 1, pp. 13901393, 2010.
[16] N. D. Lagaros and M. Papadrakakis, “Neural
network based prediction schemes of the non-
Perilaku Struktur Jembatan Baja Pelengkung Berdasarkan Spektrum Gempa (Widya Apriani, dkk)
77
linear seismic response of 3D buildings,” Adv. Eng.
Softw., vol. 44, no. 1, pp. 92115, 2012.
[17] D. Shyam, G. B. L. Chowdary, and D. R.
Mahapatra, “Structural Damage Identification Using
Artificial Neural Network and Synthetic data.”
[18] S. Ok, W. Son, and Y. M. Lim, “A study of the
use of artificial neural networks to estimate
dynamic displacements due to dynamic loads in
bridges,” J. Phys. Conf. Ser., vol. 382, no. 1, 2012.
[19] A. S. Fahmy, M. E. T. El-Madawy, and Y. Atef
Gobran, “Using artificial neural networks in the
design of orthotropic bridge decks,” Alexandria Eng.
J., vol. 55, no. 4, pp. 31953203, 2016.
[20] S. Kim, “Experimental investigation of local
damage detection on a 1/15 scale model of a
suspension bridge deck,” KSCE J. Civ. Eng., vol. 7, no.
4, pp. 461468, 2008.
[21] W. F. Darmawan, R. Suryanita, and Z.
Djauhari, “Evaluasi Kesehatan Struktur Bangunan
berdasarkan Respon Dinamik Berbasiskan Data
Akselerometer,” Media Komun. Tek. Sipil, vol. 23, no.
2, p. 142, 2017.
[22] R. Suryanita, “Prediksi Kerusakan Model
Jembatan Beton Bertulang Berdasarkan Mutu Beton
dengan Metode Jaringan Saraf Tiruan,” no.
November, pp. 368375, 2015.
[23] A. C. Neves, I. González, J. Leander, and R.
Karoumi, “Structural health monitoring of bridges:
a model-free ANN-based approach to damage
detection,” J. Civ. Struct. Heal. Monit., vol. 7, no. 5,
pp. 689702, Nov. 2017.
[24] M. Lydon, S. E. Taylor, D. Robinson, A. Mufti,
and E. J. O. Brien, “Recent developments in bridge
weigh in motion (B-WIM),” J. Civ. Struct. Heal.
Monit., vol. 6, no. 1, pp. 6981, 2016.
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