Patrick Nicholas Hadinata, Djoni Simanta, L. Eddy, K. Nagai
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.
基础设施的维修是保证混凝土结构安全的一项重要工作。然而，大多数的裂纹检测实践都是手工进行的，这是不安全的，高度主观的，耗时的。因此，需要使用人工智能来实现更准确、更高效的系统。卷积神经网络(CNN)是人工智能的一个子集，它通过语义图像分割来检测混凝土表面的裂缝。本研究的目的是比较先进的编码器-解码器架构在检测混凝土表面裂缝方面的有效性，U-Net和DeepLabV3+架构分别在生物医学和稀疏多尺度图像分割方面具有潜力。神经网络的训练使用云计算，使用高性能图形处理单元NVIDIA Tesla V100和27.4 GB RAM。本研究使用了内部和外部数据。内部数据由简单裂纹组成，用作训练和验证数据。同时，外部数据由更复杂的裂缝组成，用于进一步的测试。基于准确度、F1、精度和召回率的四个评估指标，对这两种体系结构进行了比较。U-Net的分割准确率为96.57%，F1 = 87.55%，精密度为88.15%，召回率为88.94%，DeepLabV3+的分割准确率为96.47%，F1 = 85.29%，精密度为92.07%，召回率为81.84%。实验结果(内部和外部数据)表明，这两种架构都能准确有效地分割裂缝。此外，U-Net和DeepLabV3+的性能超过了先前测试的架构，即FCN。
{"title":"Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3+","authors":"Patrick Nicholas Hadinata, Djoni Simanta, L. Eddy, K. Nagai","doi":"10.22146/jcef.65288","DOIUrl":"https://doi.org/10.22146/jcef.65288","url":null,"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.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85015322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Watershed is a multi-aspect ecological system, which functions as a source of water resources, in order to meet daily needs. It also motivates both economical and life matters, as well as serve as a sanitary channel for the surrounding community. Watershed also generates pollutants, which are known to potentially cause a decrease in river water quality. The degradation of river habitats that are caused by high pollutants penetration into the water body, decreases the capacity to carry out self-purification of toxic loads. The water pollutant load-carrying capacity is then calculated through various methods, one of which is the use of a computerized numerical modeling simulation called WASP (Water Quality Analysis Simulation Program). This method was developed by the ES-EPA, in order to process TMDLs (Total Maximum Daily Loads) data on river bodies, as well as examine each part of the water quality, based on spatial and temporal inputs. This study was conducted at the Karang Mumus Sub-watershed flowing through the centre of Samarinda City, with aims to determine the pollutants' carrying capacity, existing load, and toxic waste allocation, via the use of the BOD (Biological Oxygen Demand) technique as a parameter of water quality. The calculation was carried out by segmenting the river into five parts, based on the number of districts it passes through, during pollutant loads inventory. The WASP modeling simulation showed that the total pollutant load-carrying capacity of the whole segments was 5,670 kg/day. It also showed the existing loads of about 3,605 kg/day, with the margin having the ability to receive pollutants at 2,065 kg/day. Moreover, the allocation of pollutant loads varies for each segment, with 2, 3, and 4 observed to reduce the pollutant by 390, 220, and 10 kg/day, respectively. However, segments 1 and 5 were still allowed to receive pollutant loads up to 1,740 and 945 kg/day, respectively.
{"title":"Analysis of the Carrying Capacity and the Total Maximum Daily Loads of the Karang Mumus Sub-watershed in Samarinda City Using the WASP Method","authors":"A. Diansyukma, S. Saraswati, A. T. Yuliansyah","doi":"10.22146/JCEF.62826","DOIUrl":"https://doi.org/10.22146/JCEF.62826","url":null,"abstract":"Watershed is a multi-aspect ecological system, which functions as a source of water resources, in order to meet daily needs. It also motivates both economical and life matters, as well as serve as a sanitary channel for the surrounding community. Watershed also generates pollutants, which are known to potentially cause a decrease in river water quality. The degradation of river habitats that are caused by high pollutants penetration into the water body, decreases the capacity to carry out self-purification of toxic loads. The water pollutant load-carrying capacity is then calculated through various methods, one of which is the use of a computerized numerical modeling simulation called WASP (Water Quality Analysis Simulation Program). This method was developed by the ES-EPA, in order to process TMDLs (Total Maximum Daily Loads) data on river bodies, as well as examine each part of the water quality, based on spatial and temporal inputs. This study was conducted at the Karang Mumus Sub-watershed flowing through the centre of Samarinda City, with aims to determine the pollutants' carrying capacity, existing load, and toxic waste allocation, via the use of the BOD (Biological Oxygen Demand) technique as a parameter of water quality. The calculation was carried out by segmenting the river into five parts, based on the number of districts it passes through, during pollutant loads inventory. The WASP modeling simulation showed that the total pollutant load-carrying capacity of the whole segments was 5,670 kg/day. It also showed the existing loads of about 3,605 kg/day, with the margin having the ability to receive pollutants at 2,065 kg/day. Moreover, the allocation of pollutant loads varies for each segment, with 2, 3, and 4 observed to reduce the pollutant by 390, 220, and 10 kg/day, respectively. However, segments 1 and 5 were still allowed to receive pollutant loads up to 1,740 and 945 kg/day, respectively.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89734735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seng Hansen, Susy F. Rostiyanti, Rizaldi Rizaldi, Clara Andjarwati
The COVID-19 outbreak began at the end of 2019, and has evolved to a pandemic threatening various industries’ sustainability. Decisive actions have been taken to tackle the pandemic’s spread, however, various impacts continue to be felt by many industries, including the construction industry. This paper therefore focuses on the COVID-19 outbreak’s impact on Quantity Surveyors’ (QS) construction projects and activities, as a key profession in the industry. A mixed method approach, questionnaire survey followed by expert interviews, was adopted. Subsequently, 199 valid responses for analysis were obtained from the questionnaire distribution, using descriptive statistics and Significance Index. Furthermore, qualitative data were acquired through semi-structured interviews with five experts, and analyzed using a structured thematic analysis. According to the results, 56.78% of respondents experienced project slowdowns, 13.57% experienced project suspensions or terminations, and 12.56% experienced cost overruns. Most respondents acknowledged the pandemic had present changes to projects (84.92%), in the form of changes in organization structure, work culture, technological application, and project objectives. Meanwhile, the Significance Index has successfully established an 11-factors ranking, regarding the pandemic’s impact on QS activities, with impact on the overall project completion as the most profound impact followed by impacts on project scheduling, supply chain, tendering, cost controlling, and claim management. This study’s qualitative and quantitative findings tend to be in accordance, thus, providing some fundamental insights regarding the COVID-19 outbreak’s impact on the construction industry, including direct impacts on project sustainability, technology adoption, and project resiliency issues. In addition, this study also contributes to scientific knowledge by discussing the issues and trends of work culture changes in QS professional activities.
{"title":"Quantity Surveyors’ Response to the COVID-19 Outbreak: A Mixed Method Approach","authors":"Seng Hansen, Susy F. Rostiyanti, Rizaldi Rizaldi, Clara Andjarwati","doi":"10.22146/JCEF.60715","DOIUrl":"https://doi.org/10.22146/JCEF.60715","url":null,"abstract":"The COVID-19 outbreak began at the end of 2019, and has evolved to a pandemic threatening various industries’ sustainability. Decisive actions have been taken to tackle the pandemic’s spread, however, various impacts continue to be felt by many industries, including the construction industry. This paper therefore focuses on the COVID-19 outbreak’s impact on Quantity Surveyors’ (QS) construction projects and activities, as a key profession in the industry. A mixed method approach, questionnaire survey followed by expert interviews, was adopted. Subsequently, 199 valid responses for analysis were obtained from the questionnaire distribution, using descriptive statistics and Significance Index. Furthermore, qualitative data were acquired through semi-structured interviews with five experts, and analyzed using a structured thematic analysis. According to the results, 56.78% of respondents experienced project slowdowns, 13.57% experienced project suspensions or terminations, and 12.56% experienced cost overruns. Most respondents acknowledged the pandemic had present changes to projects (84.92%), in the form of changes in organization structure, work culture, technological application, and project objectives. Meanwhile, the Significance Index has successfully established an 11-factors ranking, regarding the pandemic’s impact on QS activities, with impact on the overall project completion as the most profound impact followed by impacts on project scheduling, supply chain, tendering, cost controlling, and claim management. This study’s qualitative and quantitative findings tend to be in accordance, thus, providing some fundamental insights regarding the COVID-19 outbreak’s impact on the construction industry, including direct impacts on project sustainability, technology adoption, and project resiliency issues. In addition, this study also contributes to scientific knowledge by discussing the issues and trends of work culture changes in QS professional activities.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84643771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pre-stressed ground anchor systems or tieback systems are commonly used at wide and irregular-shaped excavations, with the advantage of lower cost and ease of construction compared to the braced excavations, but they come with the drawback on permits for excavations near buildings and tunnels. Research on tieback systems in sands was generally conducted. However, the studies on the correlation between the retaining wall deflection and pre-stress force are few. The objectives of this paper are to study the influence of pre-stress force, depth of excavation, wall embedment length, and soil shear strength that is represented by soil friction angle on the deflection and soil pressure acting on the retaining wall. The parametric study was conducted on an excavation in sand using the finite element method with the Hardening soil model. The results showed that a 50 kN/m increase in pre-stress force reduced the wall deflection on top of the wall by 0.005–0.083% of excavation depth. However, the pre-stressing influence in reducing wall deflection at excavations became less significant along with the sand density increase due to higher friction angle contribution to excavation stability. Moreover, the pre-stress force needed for stabilization of the wall with long embedment length is smaller than those on the wall with shorter embedment length, since the embedment length increase of 0.25 times of excavation depth reduces wall top deflection by 0.002–0.095% of excavation depth. Also, the increase of soil density reduces the need for wall embedment length, so at dense sand, the embedment length of 0.5 times of excavation depth is sufficient to support the excavation.
{"title":"Influence of Pre-Stressing on Tieback Retaining Wall for Sandy Soils Excavations","authors":"Anthonius Steven Sutanto, P. Rahardjo, Aswin Lim","doi":"10.22146/jcef.61564","DOIUrl":"https://doi.org/10.22146/jcef.61564","url":null,"abstract":"Pre-stressed ground anchor systems or tieback systems are commonly used at wide and irregular-shaped excavations, with the advantage of lower cost and ease of construction compared to the braced excavations, but they come with the drawback on permits for excavations near buildings and tunnels. Research on tieback systems in sands was generally conducted. However, the studies on the correlation between the retaining wall deflection and pre-stress force are few. The objectives of this paper are to study the influence of pre-stress force, depth of excavation, wall embedment length, and soil shear strength that is represented by soil friction angle on the deflection and soil pressure acting on the retaining wall. The parametric study was conducted on an excavation in sand using the finite element method with the Hardening soil model. The results showed that a 50 kN/m increase in pre-stress force reduced the wall deflection on top of the wall by 0.005–0.083% of excavation depth. However, the pre-stressing influence in reducing wall deflection at excavations became less significant along with the sand density increase due to higher friction angle contribution to excavation stability. Moreover, the pre-stress force needed for stabilization of the wall with long embedment length is smaller than those on the wall with shorter embedment length, since the embedment length increase of 0.25 times of excavation depth reduces wall top deflection by 0.002–0.095% of excavation depth. Also, the increase of soil density reduces the need for wall embedment length, so at dense sand, the embedment length of 0.5 times of excavation depth is sufficient to support the excavation.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85860848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Debris flow is a disaster occurring in cases where a sediment particle flows at high speed, down to the slope, and usually with high viscosity and speed. This disaster is very destructive and human life-threatening, especially in mountainous areas. As one of the world’s active volcanoes in the world, Rinjani had the capacity to produce over 3 million m3 volume material in the 2015 eruption alone. Therefore, this study proposes a numerical model analysis to predict the debris flow release area (erosion) and deposition, as well as the discharge, flow height, and velocity. The Digital Elevation Model (DEM) was analyzed in ArcGIS, to acquire the Cartesian coordinates and “hillshade” form. This was also used as a method to produce vulnerable areas in the Jangkok watershed. Meanwhile, the Rapid Mass Movement Simulation (RAMSS) numerical modeling was simulated using certain parameters including volume, friction, and density, derived from the DEM analysis results and assumptions from similar historical events considered as the best-fit rheology. In this study, the release volume was varied at 1,000,000 m3, 2,000,000 m3, and 3,000,000 m3, while the simulation results show movement, erosion, and debris flow deposition in Jangkok watershed. This study is bound to be very useful in mitigating debris flow as disaster anticipation and is also expected to increase community awareness, as well as provide a reference for structural requirements, as a debris flow prevention.
{"title":"Numerical Modelling Based on Digital Elevation Model (DEM) Analysis of Debris Flow at Rinjani Volcano, West Nusa Tenggara, Indonesia","authors":"M. Qodri, Noviardi Noviardi, A. Rizqi, L. Z. Mase","doi":"10.22146/jcef.63417","DOIUrl":"https://doi.org/10.22146/jcef.63417","url":null,"abstract":"Debris flow is a disaster occurring in cases where a sediment particle flows at high speed, down to the slope, and usually with high viscosity and speed. This disaster is very destructive and human life-threatening, especially in mountainous areas. As one of the world’s active volcanoes in the world, Rinjani had the capacity to produce over 3 million m3 volume material in the 2015 eruption alone. Therefore, this study proposes a numerical model analysis to predict the debris flow release area (erosion) and deposition, as well as the discharge, flow height, and velocity. The Digital Elevation Model (DEM) was analyzed in ArcGIS, to acquire the Cartesian coordinates and “hillshade” form. This was also used as a method to produce vulnerable areas in the Jangkok watershed. Meanwhile, the Rapid Mass Movement Simulation (RAMSS) numerical modeling was simulated using certain parameters including volume, friction, and density, derived from the DEM analysis results and assumptions from similar historical events considered as the best-fit rheology. In this study, the release volume was varied at 1,000,000 m3, 2,000,000 m3, and 3,000,000 m3, while the simulation results show movement, erosion, and debris flow deposition in Jangkok watershed. This study is bound to be very useful in mitigating debris flow as disaster anticipation and is also expected to increase community awareness, as well as provide a reference for structural requirements, as a debris flow prevention.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75112410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Methane gas (CH4) is a greenhouse gas that can potentially induce global warming and it is known as surface ozone precursor. CH4 is generally produced from biological process occurred at the landfill which is not equipped with CH4 recovery and treatment system. Note that, very few of landfills in Indonesia have been operated as sanitary landfill but rather most of them act as dumping site. One landfill in West Java Province is Sarimukti Landfill which receives nearly 604,674 ton of solid waste annually. Existing studies have been using the first tier of the Intergovernmental Panel on Climate Change (IPCC) guideline for the emission estimation which provides high uncertainty due to the international default data. In addition, there are uncertainties for the multi years estimation because the kinetic rate of biological processes was not involved in the calculation. To fill in this gap, this research was conducted to use an alternative of methodology for estimating CH4 from landfill using a well known software of the Landfill Gas Emissions Model (LandGEM) which facilitates biological reaction in the calculation. We will also perform calculations using the traditional IPCC method for the Sarimukti landfill as a case study. To quantify the impact of CH4 emission, its dispersion was calculated using the AMS/EPA Regulatory Model (AERMOD). Potential impact on surface ozone formation was assessed using ozone formation potential (OFP) metric. The results of this study indicate that methane gas emissions have increased every year, where the highest emissions occurred in 2025 of 14,810.41 Mg/year (LandGEM) and 11,462.66 Mg/year (IPCC). Likewise, the potential for OFP from methane gas concentrations has increased every year where the highest concentration of surface ozone formation is in 2025 of 183,40 Mg/year. Meanwhile, the methane emission (CH4) has a dispersion pattern which is influenced by meteorological factors around the Sarimukti landfill.
{"title":"Methane Emission Estimation and Dispersion Modeling for a Landfill in West Java, Indonesia","authors":"Soni Pratamayudha Wijaya, S. Ainun, D. A. Permadi","doi":"10.22146/jcef.62824","DOIUrl":"https://doi.org/10.22146/jcef.62824","url":null,"abstract":"Methane gas (CH4) is a greenhouse gas that can potentially induce global warming and it is known as surface ozone precursor. CH4 is generally produced from biological process occurred at the landfill which is not equipped with CH4 recovery and treatment system. Note that, very few of landfills in Indonesia have been operated as sanitary landfill but rather most of them act as dumping site. One landfill in West Java Province is Sarimukti Landfill which receives nearly 604,674 ton of solid waste annually. Existing studies have been using the first tier of the Intergovernmental Panel on Climate Change (IPCC) guideline for the emission estimation which provides high uncertainty due to the international default data. In addition, there are uncertainties for the multi years estimation because the kinetic rate of biological processes was not involved in the calculation. To fill in this gap, this research was conducted to use an alternative of methodology for estimating CH4 from landfill using a well known software of the Landfill Gas Emissions Model (LandGEM) which facilitates biological reaction in the calculation. We will also perform calculations using the traditional IPCC method for the Sarimukti landfill as a case study. To quantify the impact of CH4 emission, its dispersion was calculated using the AMS/EPA Regulatory Model (AERMOD). Potential impact on surface ozone formation was assessed using ozone formation potential (OFP) metric. The results of this study indicate that methane gas emissions have increased every year, where the highest emissions occurred in 2025 of 14,810.41 Mg/year (LandGEM) and 11,462.66 Mg/year (IPCC). Likewise, the potential for OFP from methane gas concentrations has increased every year where the highest concentration of surface ozone formation is in 2025 of 183,40 Mg/year. Meanwhile, the methane emission (CH4) has a dispersion pattern which is influenced by meteorological factors around the Sarimukti landfill.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74099926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Muntohar, Gayuh Aji Prasetyaningtiyas, R. Hidayat
Severe landslides followed by debris flow were recorded to have occurred on 12 December 2014 and discovered to have ruined infrastructures and buried hundreds of peoples in Karangkobar subdistrict of Banjarnegara district, Central Java. There was, however, a high rainfall of up to 200 mm per day for two days before the disaster. Therefore, this research was conducted to predict and assess the landslide area using Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) version 2.0 model to calculate the pore water pressure and safety factor (FS) during rainfall infiltration. The TRIGRS model focused on spatial analysis. The data used as input for this analysis include the DEM, geological and geotechnical properties, infiltration variables, and rainfall intensity. Meanwhile, the FS value was observed to be lowest at the initial condition before rainfall infiltration by ranging between 1 and 1.2 and distributed at the steep slope area near Jemblung. The results were validated through the back analysis of a reference landslide event and the instability in the area was confirmed to be initiated in the 3 three hours of rainfall while the hazards area occurs majorly at the steep slopes with slope angles greater than 30o after 24 hours. The simulation results showed the steep slope area with an inclination angle greater than 30o is susceptible to failure during the rainfall infiltration due to FS < 1.2 while some locations with steep slopes were likely not to fail as indicated by FS >1.2. This study generally concluded that the TRIGRS was able to predict the location of the failure when compared with the results from the field observation of the landslide occurrences.
{"title":"The Spatial Model using TRIGRS to determine Rainfall-Induced Landslides in Banjarnegara, Central Java, Indonesia","authors":"A. Muntohar, Gayuh Aji Prasetyaningtiyas, R. Hidayat","doi":"10.22146/jcef.55282","DOIUrl":"https://doi.org/10.22146/jcef.55282","url":null,"abstract":"Severe landslides followed by debris flow were recorded to have occurred on 12 December 2014 and discovered to have ruined infrastructures and buried hundreds of peoples in Karangkobar subdistrict of Banjarnegara district, Central Java. There was, however, a high rainfall of up to 200 mm per day for two days before the disaster. Therefore, this research was conducted to predict and assess the landslide area using Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) version 2.0 model to calculate the pore water pressure and safety factor (FS) during rainfall infiltration. The TRIGRS model focused on spatial analysis. The data used as input for this analysis include the DEM, geological and geotechnical properties, infiltration variables, and rainfall intensity. Meanwhile, the FS value was observed to be lowest at the initial condition before rainfall infiltration by ranging between 1 and 1.2 and distributed at the steep slope area near Jemblung. The results were validated through the back analysis of a reference landslide event and the instability in the area was confirmed to be initiated in the 3 three hours of rainfall while the hazards area occurs majorly at the steep slopes with slope angles greater than 30o after 24 hours. The simulation results showed the steep slope area with an inclination angle greater than 30o is susceptible to failure during the rainfall infiltration due to FS < 1.2 while some locations with steep slopes were likely not to fail as indicated by FS >1.2. This study generally concluded that the TRIGRS was able to predict the location of the failure when compared with the results from the field observation of the landslide occurrences.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74195175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wilhelmus Bunganaen, John H. Frans, Yustinus A. Seran, D. Legono, D. Krisnayanti
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.
{"title":"Rainfall-Runoff Simulation Using HEC-HMS Model in the Benanain Watershed, Timor Island","authors":"Wilhelmus Bunganaen, John H. Frans, Yustinus A. Seran, D. Legono, D. Krisnayanti","doi":"10.22146/jcef.64782","DOIUrl":"https://doi.org/10.22146/jcef.64782","url":null,"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.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74913123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md. Fahad Shahriar Zawad, Md. Asifur Rahman, S. N. Priyom
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.
{"title":"Bio-Engineered Concrete: A Critical Review on The Next Generation of Durable Concrete","authors":"Md. Fahad Shahriar Zawad, Md. Asifur Rahman, S. N. Priyom","doi":"10.22146/jcef.65317","DOIUrl":"https://doi.org/10.22146/jcef.65317","url":null,"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.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81400876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan","authors":"Pollen Chakma, A. Akter","doi":"10.22146/jcef.64497","DOIUrl":"https://doi.org/10.22146/jcef.64497","url":null,"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.","PeriodicalId":31890,"journal":{"name":"Journal of the Civil Engineering Forum","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89203258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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