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Journal of the Civil Engineering Forum
Published by Universitas Gadjah Mada
ISSN : 25811037     EISSN : 25495925     DOI : -
Core Subject : Social, Engineering,
Civil Engineering, Building, Construction & Architecture Environmental Science
Journal of the Civil Engineering Forum (JCEF) is a four-monthly journal on Civil Engineering and Environmental related sciences. The journal was established in 1992 as Forum Teknik Sipil, a six-monthly journal published in Bahasa Indonesia, where the first publication was issued as Volume I/1 - January 1992 under the name of Forum Teknik Sipil.
Arjuna Subject : -
Articles 225 Documents
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Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan Pollen Chakma; Aysha Akter
Journal of the Civil Engineering Forum Vol. 7 No. 3 (September 2021)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.64497

Abstract

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165  gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.
Feasibility Evaluation of Wastewater Treatment Plant System: A Case Study of Domestic Wastewater System in Sleman Regency, Yogyakarta, Indonesia Sri Puji Saraswati; Gregorius Henry Diavid; Sophia An Nisa; Nilna Amal; Visi Asriningtyas
Journal of the Civil Engineering Forum Vol. 7 No. 3 (September 2021)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.64643

Abstract

Domestic Wastewater Treatment Plant (DWWTP) type 1 and DWWTP type 2 were being evaluated. DWWTP type 1 is located in Sembir area while DWWTP type 2 is located in Tambakrejo area which are both in Sleman Regency, Special Region of Yogyakarta (Daerah Istimewa Yogyakarta or DIY), Indonesia. The emphasis of this research is to choose the manhole material which has the least leakage to the soil, influent discharge performance and wastewater treatment quality effluent. The method used to measure the discharge was by averaging daily discharge for twelve hours, while the E. Coli bacteria under the manhole was also being analyzed. Pollution Index method was also used to evaluate the pollution levels of the wastewater treatment effluent. Results of the study indicated that DWWTP type 1 performance was not optimal because the number of users was greater than that of the design. The impacts were excessive capacity, improper detention time and several parameters of the effluent did not meet the Indonesian legal regulation, including Chemical Oxygen Demand (COD), with efficiency of 34.43%. Wastewater treatment quality effluent parameters which met the Indonesian legal regulation were pH, TSS, TDS, Oil and Grease and Chlorine for DWWTP type 1. Pollution Index (PI) of DWWTP type 1 was 7.02 and PI of DWWTP type 2 was 6.96 which were relatively categorized as moderately polluted. DWWTP type 2 performance was optimal with mean discharge lower than the design discharge. Parameters of the effluent which met the Indonesian legal regulation were pH, TSS, TDS, Oil and Grease, Detergent and COD for DWWTP type 2. The COD of DWWTP type 2 met the Indonesian legal regulation with high efficiency of 73.24%. The E. Coli bacteria was not found in soils under the ring type precast concrete manholes. Hence ring type precast concrete base manhole is recommended.
Rainfall-Runoff Simulation Using HEC-HMS Model in the Benanain Watershed, Timor Island Wilhelmus Bunganaen; John H. Frans; Yustinus Akito Seran; Djoko Legono; Denik Sri Krisnayanti
Journal of the Civil Engineering Forum Vol. 7 No. 3 (September 2021)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.64782

Abstract

Floods in a watershed area are caused by reduced water recharge due to changes in land use, increasing their discharge volume. Benanain watershed is an extensive area with many tributaries. Watershed morphometrics provides initial information about the hydrological behavior and the hydrograph shape of flooding in these areas. Furthermore, rainfall-runoff modeling uses as a unit to approach the hydrological values of the flooding process. This study determines the physical characteristics of the Benanain watershed based on curve number (CN) values, land cover, peak discharge, and peak time. It was conducted on the Benanain watershed with 29 sub-watersheds covering 3,181.521 km2. Data were collected on the rainfall experienced for 13 years from 1996 to 2008 and analyzed using the Log Pearson Type III method, while the HEC HMS model was used for flood discharge analysis. HEC-HMS model must calibrate by adjusting the model parameter values until the model results match historical data such as initial abstraction, lag time, recession, baseflow values, and curve number.  The results show that the curve number values range from 56.55 - 73.90, comprising secondary dryland forest and shrubs. Moreover, the rock lithology in the Benanain watershed is dominated by scaly clay and other rock blocks. This means the area has low to very low permeability, which affects the volume of runoff. The return period of a 1000-year flood discharge obtained a peak of 5,794.50 m3/s, with a peak time of ± 14 hours. Morphometry of the Temef watershed with large catchment, radial shape pattern, an average of steep slope river, and meandering affects the peak of flood discharge hydrograph and the peak time of the flood.  
Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3+ Patrick Nicholas Hadinata; Djoni Simanta; Liyanto Eddy; Kohei Nagai
Journal of the Civil Engineering Forum Vol. 7 No. 3 (September 2021)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.65288

Abstract

Maintenance of infrastructures is a crucial activity to ensure safety using crack detection methods on concrete structures. However, most practice of crack detection is carried out manually, which is unsafe, highly subjective, and time-consuming. Therefore, a more accurate and efficient system needs to be implemented using artificial intelligence. Convolutional neural network (CNN), a subset of artificial intelligence, is used to detect cracks on concrete surfaces through semantic image segmentation. The purpose of this research is to compare the effectiveness of cutting-edge encoder-decoder architectures in detecting cracks on concrete surfaces using U-Net and DeepLabV3+ architectures with potential in biomedical, and sparse multiscale image segmentations, respectively. Neural networks were trained using cloud computing with a high-performance Graphics Processing Unit NVIDIA Tesla V100 and 27.4 GB of RAM. This study used internal and external data. Internal data consisted of simple cracks and were used as the training and validation data. Meanwhile, external data consisted of more complex cracks, which were used for further testing. Both architectures were compared based on four evaluation metrics in terms of accuracy, F1, precision, and recall. U-Net achieved segmentation accuracy = 96.57%, F1 = 87.55%, precision = 88.15%, and recall = 88.94%, while DeepLabV3+ achieved segmentation accuracy = 96.47%, F1 = 85.29%, precision = 92.07%, and recall = 81.84%. Experiment results (internal and external data) indicated that both architectures were accurate and effective in segmenting cracks. Additionally, U-Net and DeepLabV3+ exceeded the performance of previously tested architecture, namely FCN.
Bio-Engineered Concrete: A Critical Review on The Next Generation of Durable Concrete Md. Fahad Shahriar Zawad; Md. Asifur Rahman; Sudipto Nath Priyom
Journal of the Civil Engineering Forum Vol. 7 No. 3 (September 2021)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.65317

Abstract

Concrete is a prerequisite material for infrastructural development, which is required to be sufficiently strong and durable. It consists of fine, coarse, and aggregate particles bonded with a fluid cement that hardens over time. However, micro cracks development in concrete is a significant threat to its durability. To overcome this issue, several treatments and maintenance methods are adopted after construction, to ensure the durability of the structure. These include the use of bio-engineered concrete, which involved the biochemical reaction of non-reacted limestone and a calcium-based nutrient with the help of bacteria. These bio-cultures (bacteria) act as spores, which have the ability to survive up to 200 years, as they are also found to start the mineralization process and the filling of cracks or pores when in contact with moisture. Previous research proved that bio-engineered concrete is a self-healing technology, which developed the mechanical strength properties of the composite materials. The mechanism and healing process of the concrete is also natural and eco-friendly. Therefore, this study aims to critically analyze bio-engineered concrete and its future potentials in the Structural Engineering field, through the use of literature review. The data analysis was conducted in order to provide gradual and informative ideas on the historical background, present situation, and main mechanism process of the materials. According to the literature review, bio-engineered concrete has a promising outcome in the case of strength increment and crack healing. However, the only disadvantage was its less application in the practical fields. The results concluded that bio-engineered concrete is a new method for ensuring sustainable infrastructural development. And also, it indicated that more practical outcome-based analysis with extensive application in various aspects should be conducted, in order to assess the overall durability.

Page 23 of 23 | Total Record : 225

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