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

Penggunaan Limbah Serbuk Kayu untuk Campuran Pembuatan Bata Ringan Hariskon

Kondisi pandemi berdampak pada penjualan produk bata ringan Hariskon yang diproduksi PT. Harista Karsa Mandiri (Mitra I). Oleh karena itu, perlu adanya inovasi dalam mengembangkan produk yang sudah ada dengan memanfaatkan limbah disekitar sebagai bahan baku. Guna untuk meningkatkan daya saing produk, yaitu dengan penggunaan limbah serbuk kayu dari pengetaman UD. Harapan Baru (Mitra II) sebagai bahan pengganti sebahagian semen. Produk inovasi bata ringan menggunakan campuran antara semen, pasir, busa, air, dan serbuk kayu dengan komposisi perbandingan 1:1:0,17:0,5:0,1 dari berat semen. Adapun tahapan dalam kegiatan ini meliputi tahapan persiapan yaitu melakukan survei dan wawancara untuk memperoleh data awal yang dibutuhkan, tahapan pelaksanaan yaitu melakukan sosialisasi dan pelatihan tentang penggunaan limbah serbuk kayu dalam campuran bata ringan, tahap pendampingan pembuatan produk inovasi bata ringan, serta diakhiri tahapan evaluasi kegiatan. Hasil kegiatan ini dapat dilihat dari tingkat kepuasan mitra dengan respon mitra 87% sangat puas, 10% puas, dan 3% cukup puas

Effects of few layers graphene addition, aggregate size, and water acidity on the compressive strength and morphology of cellular lightweight concrete

Cellular Lightweight Concrete (CLC) with the addition of Few Layers Graphene (FLG) has been fabricated and characterized for canal blocks application. The CLC-FLG composite was made by mixing fine agregate (sand), cement, fly ash, water, and FLG. The compressive strength properties of the composite was tested using a digital compressive strength test to determine the effects of FLG addition, sand size gradations, and environmental acidity on the compressive strength of the composite. Meanwhile, the composite morphology was examined using Scanning Electron Microscopy (SEM). The increase in FLG content and concentrations increased the compressive strength. The highest compressive strength was shown by the composite with the highest FLG addition (15%) and without sand size gradation, namely 5.19 Mpa or there was an increase of 15.6% compared to CLC without the addition of FLG. The level of water acidity relatively did not affected the compressive strength of CLC-FLG composite. Morphological analysis showed that the addition of FLG resulted in a denser structure and reduced porosity of CLC. The CLC-FLG composite can be used as canal blocks materials for peatland restoration

Response prediction of multi-story building using backpropagation neural networks method

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

Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations

Pile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening the construction timeline, and providing safety construction. Hence, the aim of this study is the developments of statistical and artificial intelligence (AI) models for predicting bearing capacities of 141 piles. At the preliminary of the study, features or inputs of this study to predict PBC were selected trough simple regression analysis. Then, this study presents different kernels of support vector machine (SVM) technique, i.e., the dot, the radial basis function (RBF), the polynomial, the neural, and the ANOVA to predict the PBC. The aforementioned models were evaluated by several performance indices and their results were compared using a simple ranking system. The results showed that the SVM-RBF model is able to achieve the highest coefficient of determination, R2 values which are 0.967 and 0.993 for training and testing stages, respectively. It is important to mention that a multiple regression model was also employed to predict PBC values. The other SVM kernels were provided a high degree of accuracy for estimating PBC, however, the SVM-RBF model is recommended to be used as a powerful, highly reliable, and simple solution for PBC prediction

Prediction of Structural Response Based on Ground Acceleration using Artificial Neural Networks

This study utilizes Artificial Neural Networks to predict the structural responses multi-story reinforced concrete building based on ground acceleration. The strong ground acceleration might cause the catastrophic collapse of the multi-story building which leads to casualties and property damages. Therefore, it is imperative to properly design the multi-story building against the seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. Modal response spectrum analysis is performed to simulate ground acceleration 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. Ground acceleration parameters from 34 provinces in Indonesia, soil condition, and building geometry are selected as input parameters, whereas structural responses consisting of acceleration, velocity, and displacement (story drift) are selected as output parameters for the ANN. As many as 6345 datasets are used to train the ANN. From the overall datasets, 4590 data sets (72%) are used for training process, 877 data sets (14%) for the validation process, and 878 data sets (14%) for testing. The trained ANN is capable to predict structural responses based on ground acceleration at (96%) rate of prediction and the calculated Mean-Squared Errors (MSE) as low as 1.2.10−4. The high accuracy of structural response prediction can greatly assist the engineer to identify the building condition rapidly and plan the building maintenance routinely

Comparative study on prediction of axial bearing capacity of driven piles in granular materials

Estimation of axial bearing capacity plays an essential role in pile design. A part from semi-empirical and numerical methods, axial bearing capacity of piles can be either predicted by means of a maintain load test or dynamic load test. The latter test is based on wave equation analysis and it is provided by Pile driving analyzer (PDA). Combination of wave equation analysis with dynamic monitoring of the pile can result in prediction of axial bearing capacity of the pile and its distribution. This paper compares the axial capacity of pile obtained from PDA records and maintain load test (static load test) with predicted axial capacities obtained using analytical, empirical and finite element analysis. From the results it is observed that axial bearing capacity derived from numerical modelling with the aid of the finite element code, Plaxis, is in a good agreement with estimated axial capacity through analytical-empirical methods, PDA, and maintain load tes

Penentuan Muka Air dan Jenis Lapisan Tanah Menggunakan Metode Geolistrik

The tools that are often used, such as sondir and SPT, are quite heavy and require a long measurement time span, making it difficult to determine the shear strength of the soil, both in the laboratory and in the field.. This study uses the resistivity geoelectric method of the Wenner Alpha configuration supported by the Naniura NRD 300 HF tool. The aim is to determine the estimated groundwater level and determine the resistivity value of each type of soil layer below the surface of the study area. This study performs 2D measurements to calculate 1-D and 2-D geoelectric resistivity with electrodes arranged lengthwise to form a straight line. Using the IP2WIN software, the measurement results are processed to produce a 2D apparent resistivity section that describes the color image distribution values of the soil surface layer. The measurement results on track 1 shows a resistivity value between 3,512 – 1.539 Ω.m with a length of 90 m at a depth to 15.05 m. The predicted groundwater level elevation is located 0.656 meters below the surface of the ground. Track 2 has a span length of 60 meters and resistivity values ranging from 29.55 to 207.1.m, with a predicted groundwater level at a depth of 0.72 m below the surface. The types of soil layers are clay mixed with sand, sand mixed with gravel and sandstone mixed with gravel.Alat-alat yang sering digunakan seperti SPT dan sondir cukup berat dan memerlukan rentang waktu pengukuran yang lama sehingga sulit untuk menentukan kuat geser tanah baik di laboratorium maupun di lapangan. Studi ini menggunakan metode geolistrik resistivitas konfigurasi Wenner Alpha didukung alat Naniura NRD 300 HF. Tujuannya untuk mengetahui perkiraan tinggi muka air tanah dan menentukan nilai resistivitas masing-masing jenis lapisan tanah di bawah permukaan daerah studi. Studi ini melakukan pengukuran 2D untuk menghitung tahanan jenis geolistrik 1-D dan 2-D dengan elektroda di susun memanjang membentuk garis lurus. Hasil pengukuran diolah menggunakan software IP2WIN untuk mendapatkan penampang resistivitas semu 2D yang menggambarkan nilai sebaran lapisan yang permukaan tanahnya ditunjukkan pada citra berwarna. Hasil pengukuran pada lintasan 1 menunjukkan nilai resistivitas antara 3,512 – 1.539 Ω.m dengan Panjang bentang 90 m pada kedalaman sampai 15,05 m. Ketinggian muka air tanah diprediksi terletak 0,656 m di bawah permukaan tanah. Lintasan 2 memiliki panjang bentang 60 m dan nilai resistivitas berkisar antara 29,55 hingga 207,1 m, dengan prediksi muka air tanah pada kedalaman 0,72 m di bawah permukaan. Jenis lapisan tanah berupa lempung bercampur pasir, pasir bercampur kerikil dan batu pasir bercampur kerikil

Artificial Neural Network Model for Prediction of Bearing Capacity of Driven Pile

Abstract. This paper presents the development of ANN model for prediction of axial capacity of a driven pile based on Pile Driving Analyzer (PDA) test data. As many as 300 sets of high quality test data from dynamic load test performed at several construction projects in Indonesia and Malaysia were selected for this study.Input considered in the modeling are pile characteristics (diameter, length as well as compression and tension capacity), pile set, and hammer characteristics (ram weight, drop height, and energy transferred).An ANN model (named: ANN-HM) was developed in this study using a computerized intelligent system for predicting the total pile capacity as well as shaft resistance and end bearing capacity for various pile and hammer characteristics. The results show that the ANN-HM serves as a reliable prediction tool to predict the resistance of the driven pile with coefficient of correlation (R) values close to 0.9 and mean squared error (MSE) less than 1% after 15,000 number of iteration process. Abstrak. Makalah ini menyajikan pengembangan model ANN untuk prediksi kapasitas daya dukung axial tiang pancang berdasarkan data uji Pile Driving Analyzer (PDA). Sebanyak 300 set data uji dari uji beban dinamis yang dilakukan pada beberapa proyek konstruksi di Indonesia dan Malaysia dipilih untuk penelitian ini. Variabel bebas yang digunakan adalah karakteristik tiang pancang (diameter, panjang serta kapasitas tekan dan tarik), set, dan karakteristik palu penumbuk tiang (berat palu, tinggi jatuh dan energi yang ditransfer). Model ANN (yang dinamakan: ANN-HM) dikembangkan dalam penelitian ini menggunakan intelligent system dalam ANN untuk memprediksi daya dukung tiang total yang didistribusikan kepada tahanan ujung dan tahanan sisi untuk berbagai jenis tiang dan palu penumbuk tiang. Hasil penelitian menunjukkan bahwa ANN-HM dapat diandalkan untuk memprediksi daya dukung tiang pancang dengan koefisien korelasi (R) mendekati 0,9 dan rata-rata kesalahan kuadrat (MSE) kurang dari 1 % setelah 15.000 kali proses iterasi

Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

The application of artificial neural network (ANN) in predicting pile bearing capacity is underlined in several studies. However, ANN deficiencies in finding global minima as well as its slow rate of convergence are the major drawbacks of implementing this technique. The current study aimed at developing an ANN-based predictive model enhanced with genetic algorithm (GA) optimization technique to predict the bearing capacity of piles. To provide necessary dataset required for establishing the model, 50 dynamic load tests were conducted on precast concrete piles in Pekanbaru, Indonesia. The pile geometrical properties, pile set, hammer weight and drop height were set to be the network inputs and the pile ultimate bearing capacity was set to be the output of the GA-based ANN model. The best predictive model was selected after conducting a sensitivity analysis for determining the optimum GA parameters coupled with a trial-and-error method for finding the optimum network architecture i.e. number of hidden nodes. Results indicate that the pile bearing capacities predicted by GA-based ANN are in close agreement with measured bearing capacities. Coefficient of determination as well as mean square error equal to 0.990 and 0.002 for testing datasets respectively, suggest that implementation of GA-based ANN models as a highly-reliable, efficient and practical tool in predicting the pile bearing capacity is of advantage