The Application of Artificial Neural Networks in Predicting Structural Response of Multistory Building in The Region of Sumatra Island

Artificial Neural Network (ANN) method is a prediction tool which is widely used in various fields of application. This study utilizes ANN to predict structural response (story drift) of multi-story reinforced concrete building under earthquake load in the region of Sumatera Island. Modal response spectrum analysis is performed to simulate earthquake loading and produce structural response data for further use in the ANN. The ANN architecture comprises of 3 layers: an input layer, a hidden layer, and an output layer. Earthquake load parameters from 11 locations in Sumatra Island, soil condition, and building geometry are selected as input parameters, whereas story drift is selected as output parameter for the ANN. As many as 1080 data sets are used to train the ANN and 405 data sets for testing. The trained ANN is capable of predicting story drift under earthquake loading at 95% rate of prediction and the calculated Mean-Squared Errors (MSE) as low as 1.6.10-4. The high accuracy of story drift prediction is more than 90% can greatly assist the engineer to identify the building condition rapidly due to earthquake loads and plan the building maintenance routinely

Integrated bridge health monitoring, evaluation and alert system using neuro-genetic hybrids

The bridge monitoring system which can analyze and predicts damage level of bridges due to earthquake loads is not yet available in Malaysia. Even though Malaysia is not an earthquake-prone country, earthquake from neighboring countries could affect the stability of the existing bridges in Malaysia. This study aims to analyze the performance of the bridge subject to earthquake loads and develop the intelligent monitoring system to predict the bridge health condition. The case study is the Second Penang Bridge Package-3B. The Intelligent System consists of the Artificial Neural Networks (ANN) and Genetic Algorithm (GA) hybrid model to obtain the optimum weight in the prediction system. The ANN inputs are 4633 data of the bridge response accelerations and displacements while the outputs are the bridge damage levels. Damage levels are obtained through nonlinear time history analyses using SAP2000. The damage level criterion is based on FEMA 356 focusing on Immediate Occupancy (IO), Life Safety (LS) and Collapse Prevention (CP) level. This intelligent monitoring system will display the alert warning system based on the prediction results with green for IO, yellow for LS and Red color for CP level. According to the results, the best performance of the displacement as data input in the prediction system is 2.2% higher than the acceleration data. This study is verified with pushover-static test to the mini-scale piers model in ratio 1:34. The first crack occurred on the base of Pier 1 when the lateral load is 9 kN, 12 kN for Pier 2 and 8 kN for Pier 4. Maximum displacement at Pier 1 is 10 mm while at Pier 2 and Pier 4 is 6 mm individually. The intelligent monitoring system can greatly assist the bridge authorities to identify the bridge health condition rapidly and plan the bridge maintenance routinely

Intelligent Monitoring System on Prediction of Building Damage Index using Neural-Network

An earthquake potentially destroys a tall building. The building damage can be indexed by FEMA into three categories namely immediate occupancy (IO), life safety (LS), and collapse prevention (CP). To determine the damage index, the building model has been simulated into structure analysis software. Acceleration data has been analyzed using non linear method in structure analysis program. The earthquake load is time history at surface, PGA=0105g. This work proposes an intelligent monitoring system utilizing artificial neural network to predict the building damage index. The system also provides an alert system and notification to inform the status of the damage. Data learning is trained on ANN utilizing feed forward and back propagation algorithm. The alert system is designed to be able to activate the alarm sound, view the alert bar or text, and send notification via email to the security or management. The system is tested using sample data represented in three conditions involving IO, LS, and CP. The results show that the proposed intelligent monitoring system could provide prediction of up to 92% rate of accuracy and activate the alert. Implementation of the system in building monitoring would allow for rapid, intelligent and accurate prediction of the building damage index due to earthquake

Damage Level Prediction of Pier using Neuro-Genetic Hybrid

Generally, long span bridges have multiple columns as known as piers to support the stability of the bridge. The pier is the most vulnerable part of the deck against the earthquake load. The study aims to predict the performance of the pier on the bridge structure subject to earthquake loads using a Neuro-Genetic Hybrid. The mix design of the Back Propagation Neural Networks (BPNN) and Genetic Algorithm (GA) method obtained the optimum-weight factors to predict the damage level of a pier. The input of Neuro-Genetic hybrid consists of 17750 acceleration-data of bridge responses. The outputs are the bridge-damage levels based on FEMA 356. The categorize of a damage level was divided into four performance levels of the structure such as safe, immediate occupancy, life safety, and collapse prevention. Bridge responses and performances have resulted through analysis of Nonlinear Time History. The best of Mean Squared Error and Regression value for the Neuro-Genetic hybrids method are 0.0041 and 0.9496 respectively at 50000 epochs for the testing process.  The Regression value denotes the predicted damage values more than 90% closer to the actual damage values. Thus, the damage level prediction of the pier in this study offers as an alternative to structural control and monitor of bridges

RESPONS STRUKTUR SDOF AKIBAT BEBAN SINUSOIDAL DENGAN METODE INTEGRAL DUHAMEL

In engineering field, the simplicity process is often done in order to make easier the processing. Some buildings e.g houses, tower and others can be idealized as Single Degree of Freedom System (SDOF) with an assumption that the response caused by dynamical load only taken place in horizontall direction. To detect the response occurs cause by dynamical load, e.g displacement, velocity, and acceleration maximum structure, therefore the response of structure concept can be used. In this case, response of the structure is calculated numerically using Duhamel’s Integral. First, the external force put on the system, both on mass and restrain and then calculating the response that occurred. The calculation can be repeat for any different parameters. The parameter variation are mass and system stiffness. The response of the structure is calculated with sinusoidal loading type for dumping system and undumping system. The study results showed that the maximum removal will increase at the mass added twice and decrease at the reducing load a half. The removal is in proportion to the mass of the system and inversely proportion to the stiffness of the system.     Abstract in Bahasa Indonesia:   Dalam bidang teknik, penyederhanaan proses perhitungan sering dilakukan untuk memudahkan pengolahan data. Beberapa struktur bangunan seperti rumah, tower dan bangunan lainnya dalam proses perhitungan dapat disederhanakan dengan mengidealisasikan bangunan tersebut sebagai sistem dengan derajat kebebasan tunggal (SDOF). Dalam sistem SDOF respons struktur yang terjadi akibat beban dinamik diasumsikan searah horizontal. Untuk mendapatkan respons yang terjadi disebabkan oleh beban dinamik seperti perpindahan, kecepatan dan percepatan maksimum dapat digunakan konsep respons struktur. Dalam penelitian ini respons struktur dihitung secara numerik menggunakan Integral Duhamel. Langkah perhitungan dimulai dengan menempatkan gaya luar pada sistem struktur dan dihitung nilai respons yang dihasilkan. Perhitungan dapat diulang untuk beberapa parameter yang berbeda. Parameter yang divariasikan adalah massa dan kekakuan system. Respons struktur dihitung dengan tipe pembebanan Sinusiodal untuk sistem teredam dan system tak teredam. Hasil perhitungan memperlihatkan untuk kekakuan tetap perpindahan maksimum akan meningkat pada saat massa sistem ditambah dua kali semula, dan akan menurun jika massa system dikurangi setengah dari massa mula-mula. Hal ini berbanding terbalik terhadap variasi kekakuan dengan massa sistem tetap.   Kata kunci : respons struktur, Integral Duhamel, pembebanan sinusoidal, massa, kekakua

Prediksi Tingkat Keruntuhan Kolom Beton Bertulang Akibat Pembebanan Statik Menggunakan Jaringan Saraf Tiruan (JST)

Column failure is one of failure condition in building that most anticipated in civil engineering world, so in designing column required more accurate calculation. One of solution in order to calculating column failure faster and more accurate is using Artificial Neural Network (ANN). ANN imitate how brain working and used to predict column failure. In this research, ANN used to predict reinforced concrete column damage level (DL) thatloaded by static load with variation in: column section dimension, concrete ultimate capacity, longitudinal reinforcement, and steel ultimate capacity. With all variation, total data used in this research is 10962 data. In this research, training and testing composition used is 70:30, hence total data for training data is 7673 data and for testing is 3289 data. Damage level calculated by dividing column strain from finite element software analysis with strain limit from SNI 2847-2013. In this research, column damage level noted as 0 if DL less than 1 and that mean column do not reach failure level, while column damage level noted as 1 if DL more equal than 1 and that mean column reach failure level. Result from testing show that ANN accuracy in predicting damage level reach 98%. This results show ANN can be used for predicting damage level faster and accurate, as well can be used as reference for designing column

Analisis Respons Struktur Portal Baja Bertingkat Akibat Kandungan Frekuensi Gempa yang Berbeda

Indonesia is one of the countries that located in the quake zone. But not all earthquakes that occur is a devastating earthquake. Some earthquake parameters that affect the level of damage from a building structure are the peak ground acceleration, response spectrum value, earthquake duration, and earthquake frequency content. The earthquake frequency content parameters were considered the most influential on structural damage. The objective of this research is to get the response from the structure of multilevel steel portals such as displacement, inter-story drift, velocity, acceleration, and to analyze the displacement limit based on SNI 1729-2012. The reviewed structure is an open frame steel building model that is into 5 levels, 10 levels, and 15 levels. This study use time history analyses with 9 earthquake recordings of the Kobe earthquake, Mexico earthquake, Nepal earthquake, Chile earthquake, New Zealand earthquake, Sumatera earthquake, Fredericksburg earthquake, Mentawai earthquake, and Northridge earthquake that has been grouped into low-frequency content, medium frequency content, and high-frequency content. The results showed that the structure responses such as displacement, velocity, and acceleration will increase with the increasing number of levels of the building structure. The inter-story drift the allowed level of the structure still qualified based on SN 1729-2012 where the allowed drift in 7 cm and the inter-story drift produced by the structure is still less than 7 cm. An earthquake with low-frequency content has an enormous influence on the structure response in all the level structure