Studi Mengenai Pengaruh Ukuran Maksimum Agregat Kasar pada Campuran Beton Memadat Mandiri (SCC). (Hal. 62-73)

ABSTRAKBeton memadat mandiri (SCC) adalah beton inovatif yang tidak memerlukan getaran pada saat proses pelaksanaanya. Beton ini diberikan zat tambah berupa superplasticizer pada campurannya agar dapat mengalir. Berdasarkan EFNARC, beton ini harus memenuhi 3 (tiga) kriteria yaitu filling ability, passing ability dan segregation resistance. Oleh karena itu dilakukan penelitian untuk mengetahui sifat beton segar dan beton keras pada campuran beton SCC dengan ukuran agregat kasar maksimum 10 mm dan 20 mm. Kemudian komposisi campuran juga dibedakan berdasarkan modulus kehalusan gabungan agregat yang seragam dan bervariasi. Kuat tekan target pada campuran beton SCC 27 MPa dan 47 MPa.Perancangan campuran beton SCC untuk penelitian ini menggunakan cara SNI yang dimodifikasi dengan metode Dreux. Kadar superplasticizer yang digunakan untuk semua jenis campuran sebesar 1,5% dari berat semen. Hasil pengujian beton SCC menunjukan campuran mempunyai karakteristik yang relatif seragam jika modulus kehalusan agregat campuran mempunyai nilai yang sama.Kata kunci: beton memadat mandiri (SCC), modulus kehalusan, ukuran maksimum agregat, superplasticizer ABSTRACTSelf Compacting Concrete (SCC) is an innovative concrete that doesn’t require vibration during the process. This concrete is added with a superplasticizer in the mixture. Based on EFNARC, this concrete must fulfill 3 (three) conditions, such asfilling ability, passing ability and segregation resistance. Therefore, a study was conducted to determine the characteristic of fresh concrete and hard concrete in SCC mixtures with maximum size of 10-mm and 20-mm coarse agregates. The target compressive strength of SCC mixturesis 27 MPa and 47 MPa. In this study,the design of the SCC mixtures uses the SNI method that modified by the Dreux Method. The superplasticizer content used for all types of SCC mixtures is 1.5% of the weight of cement. The SCC test result shows the mixtures have relatively uniform characteristics if the fineness modulus of aggregate has the same value.Keywords: self-compacting concrete (SCC), fineness modulus, maximum agregate size, superplasticizer

Determining of Shear Modulus for the Samples Circular and Hollow Circular Cross-Section

The contribution is focused on estimating the shear modulus of the samples of circular and hollow circular sections by static method. The samples were loaded by simple torsion, individual sections were stressed by shear stress. Theoretical basis are determined by linear elasticity and strength theory and they define the relation between shear modulus, maximum shear stress and relative strains. Relative strains are determined by using measurement apparatus and measurement system Quantum X MX 840.

Design of periodic unit cell in cellular materials with extreme properties using topology optimization

This paper presents a topology optimization method to design periodic unit cell in cellular materials with extreme properties using a moving iso-surface threshold method. The aim is to determine the optimal distribution of material within the periodic unit cell. The effective properties of cellular material are obtained by using a finite element-based homogenization method. The penalty function approach is introduced to construct the objective function for designing material with extreme properties under condition of square or isotropic symmetry. New characteristic response functions of moving iso-surface threshold are proposed for maximum shear or bulk modulus, maximum shear modulus or negative Poisson’s ratio under isotropic symmetry. Several examples are presented and the results are compared to those obtained with the solid isotropic material with penalization method to demonstrate the validity of the method. A series of new and interesting microstructures with extreme properties are found and presented.

GPS and Acceleration Data in Multimode Trip Data Recognition Based on Wavelet Transform Modulus Maximum Algorithm

The GPS-based travel survey is an emerging data collection method in transportation planning. The survey's application in trip mode detection has been explored in many studies. Most research on trip mode detection methods based on GPS data has been developed and tested with data collected from European and American countries. The methods cannot be easily adapted to Asian countries such as China, India, and Japan, which have much higher population densities, more complex road networks, and highly mixed travel modes during daily commuting. Furthermore, for trip segment division in multimode travel, existing algorithms use travel time and distance thresholds that are highly dependent on local travel behavior and lack universality across traffic environments. This paper proposes an innovative framework for detecting trip modes in complex urban environments. First, a smartphone application, GPSurvey, was developed to collect passive GPS trace data. Then a wavelet transform modulus maximum algorithm was developed for trip segment division. The algorithm has outstanding capabilities for identifying singularity features of a signal; this factor suits the task of detecting mode changes in a complex traffic environment. A neural network module was developed for mode detection on the basis of cell phone GPS location and acceleration data. The results indicate that the proposed method has promising performance. The average absolute detection error of mode transfer time was within 1 min, and the accuracy for detecting all modes was greater than 85%.

A Novel Multiscale Edge Detection Approach Based on Nonsubsampled Contourlet Transform and Edge Tracking

Edge detection is a fundamental task in many computer vision applications. In this paper, we propose a novel multiscale edge detection approach based on the nonsubsampled contourlet transform (NSCT): a fully shift-invariant, multiscale, and multidirection transform. Indeed, unlike traditional wavelets, contourlets have the ability to fully capture directional and other geometrical features for images with edges. Firstly, compute the NSCT of the input image. Secondly, theK-means clustering algorithm is applied to each level of the NSCT for distinguishing noises from edges. Thirdly, we select the edge point candidates of the input image by identifying the NSCT modulus maximum at each scale. Finally, the edge tracking algorithm from coarser to finer is proposed to improve robustness against spurious responses and accuracy in the location of the edges. Experimental results show that the proposed method achieves better edge detection performance compared with the typical methods. Furthermore, the proposed method also works well for noisy images.