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INDONESIA
Civil Engineering Journal
Published by C.E.J Publishing Group
ISSN : 24763055     EISSN : 24763055     DOI : -
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture
Civil Engineering Journal is a multidisciplinary, an open-access, internationally double-blind peer -reviewed journal concerned with all aspects of civil engineering, which include but are not necessarily restricted to: Building Materials and Structures, Coastal and Harbor Engineering, Constructions Technology, Constructions Management, Road and Bridge Engineering, Renovation of Buildings, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Highway Engineering, Hydraulic and Hydraulic Structures, Structural Engineering, Surveying and Geo-Spatial Engineering, Transportation Engineering, Tunnel Engineering, Urban Engineering and Economy, Water Resources Engineering, Urban Drainage.
Arjuna Subject : -
Articles 17 Documents
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Search results for , issue "Vol 9, No 9 (2023): September" : 17 Documents clear
Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing Jose Manuel Palomino Ojeda; Billy Alexis Cayatopa-Calderón; Lenin Quiñones Huatangari; Jose Luís Piedra Tineo; Manuel Emilio Milla Pino; Wilmer Rojas Pintado
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-01

Abstract

Cracks in concrete cause structural damage, and it is important to identify and classify them. The objective of the research was to describe the behavior and predict the type of failure in concrete cylinders using convolutional neural networks. The methodology consisted of creating a database of 2650 images of failure types in concrete cylinders tested in compression at the Laboratory of Testing and Strength of Materials of the National University of Jaen, Cajamarca, Peru. To identify cracks on the concrete surface, the database was divided into training (60%), validation (20%), and testing (20%), and a transfer learning approach was developed using the MobileNet, DenseNet121, ResNet50, and VGG16 algorithms from the Keras library, programmed in Python. To validate the performance of each model, the following indicators were used: recall, precision, and F1 score. The results show that the models studied correctly classified the type of failure in concrete with accuracies of 96, 91, 86, and 90%, with the MobileNet algorithm being the best predictor with 96%. The novelty of the study was the development of deep learning algorithms with different architectures that can be used in structural health assessment as an automated and reliable method compared to traditional ones. In addition, these trained algorithms can be used as source code in drones for structural monitoring. Doi: 10.28991/CEJ-2023-09-09-01 Full Text: PDF
Influential and Intellectual Structure of Geopolymer Concrete: A Bibliometric Review Salam Al-Kasassbeh; Jafar Al-Thawabteh; Eslam Al-Kharabsheh; Amani Al-Tamseh
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-017

Abstract

The objective of this bibliometric review is to deliver an in-depth examination of the dynamic field of geopolymer concrete, revealing its evolution, current trends, and possible future trajectories. The method involves a rigorous bibliometric analysis of research output since the field's inception in 2003, underlining key milestones and mapping research patterns. Findings show a consistent surge in geopolymer concrete research, exemplified by over 1360 annual publications, with notable contributions predominantly from Australia and India. The paper also uncovers the increasing practical applications of geopolymer concrete, especially in construction processes, underpinned by a wealth of research on fly ash, a crucial manufacturing component. Additionally, prevalent research themes include compressive strength, fly ash, and geopolymer itself. The review's novelty lies in its comprehensive overview of geopolymer concrete research, elucidating past and present trends and identifying potential future research areas. It thereby serves as a firm foundation for further studies, fostering continued growth in this promising field. Doi: 10.28991/CEJ-2023-09-09-017 Full Text: PDF
The Impact of Aspect Ratio, Characteristic Strength and Compression Rebars on the Shear Capacity of Shallow RC Beams Ahmed A. Soliman; Dina M. Mansour; Ayman H. Khalil; Ahmed Ebid
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-012

Abstract

This paper investigates the impact of the aspect ratio, the characteristics strength of the concrete, and the compression steel ratio on the shear capacity of wide-shallow beams. An experimental program consists of seven specimens, including a control specimen, all tested under a three-point load test. Three specimens were considered for each parameter (the control specimen was included in all three variables). The experimental results were compared to the theoretical values of six different codes of practice; they were also analyzed to determine the ductility, stiffness, and dissipated energy of each specimen. The results indicated that the shear reinforcement was fully functioning until it yielded, with a minimum contribution of 55% of the total shear capacity of the specimens. The aspect ratio and the characteristic strength had a notable impact on the shear capacity of the specimens, while the compression steel ratio had a minor effect on the shear capacity, but it improved the stiffness and the ductility of the beams. Theoretical concrete shear strengths from design codes ranged between 77 and 163% of the experimental values; EN-1992 was the closest code to the experimental results. A comparison between the experimental results and predicted values using GP and EPR methods from previous research showed accuracies of 72% and 81%, respectively. Doi: 10.28991/CEJ-2023-09-09-012 Full Text: PDF
Potential Erosion in Mining, Oil Palm Plantations, and Watersheds Reforestation Areas Ahmad Syarif Sukri; M. Saripuddin; . Nasrul; Romy Talanipa
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-07

Abstract

Erosion forecasting is a complex issue generated by numerous causes, the extent of which varies based on the unique area and conditions. Changes in rainfall, land cover, and watershed function are the primary causes of increased erosion. This study aims to scrutinize the actual and potential erosion in the mining area (MA), oil palm plantations (OPP), and watersheds reforestation (WR) in Asoloe, South Konawe, Indonesia. We utilized qualitative research methods and surveys with the USLE model. MA shares the highest actual erosion with 332.30 tons/ha/year, with an average erosion of 27.69 tons/ha/year from 2011 to 2022. Meanwhile, the potential erosion is 4747.19 tons/ha/year, with an average of 395.60 tons/ha/year. In terms of current conditions, 44.6% of rainfall engenders erosion with more than 0.5 t/ha and 33.9% with more than 1 t/ha. This study successfully demonstrates that for given location and area characteristics, high amounts of rainfall and changes in land function eminently affect soil erosion and that the potential erosion changes that occur in the Asoloe watershed every year are exceptionally influenced by changes in land use and land function. Therefore, some mitigation strategies and policies must be taken to reduce the risk of future erosion. Doi: 10.28991/CEJ-2023-09-09-07 Full Text: PDF
Research on Rainfall Intensity Threshold of Occasional Debris Flow Based on Infiltration Hanqiang Wang; Xiangpeng Ji; Yanping Wang
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-02

Abstract

The rainfall warning method for debris flows usually uses rainfall intensity and duration to establish an I-D relationship internationally and determine the rainfall warning threshold for debris flows. This method requires extensive rainfall data from debris flow events in the study area to establish the I-D relationship. However, some areas with occasional debris flows lack sufficient debris flow events to establish I-D relationships to determine rainfall warning thresholds. Therefore, this study uses the infiltration effect of water flow on gravel soil and establishes a rainfall intensity threshold judgment formula for debris flow initiation based on the limit equilibrium method. Taking the Taiqing debris flow that occurred in Laoshan, China, on June 13, 2018, as an example, the rainfall intensity and characteristics of the debris flow are analyzed. The maximum rainfall intensity during this rainfall process far exceeds the rainfall intensity threshold determined by the judgment formula. Using the judgment formula, it can be determined that the rainfall process will cause debris flow. The judgment result is consistent with the actual situation (where a debris flow occurred during the rainfall process). To further verify the accuracy of the judgment formula, the rainfall process of Typhoon Lichma on August 11, 2019, in the study area was analyzed. The rainfall process has a long history. Still, the rainfall intensity is much lower than the threshold of rainfall intensity for the initiation of debris flow, so this rainfall will not cause the occurrence of debris flow. The judgment result is consistent with the actual situation (no debris flow occurred during rains). Doi: 10.28991/CEJ-2023-09-09-02 Full Text: PDF
Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition Siti Fatimah Abdul Razak; S. N. M. Sayed Ismail; Sumendra Yogarayan; Mohd Fikri Azli Abdullah; Noor Hisham Kamis; Azlan Abdul Aziz
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-013

Abstract

Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains. Doi: 10.28991/CEJ-2023-09-09-013 Full Text: PDF
Carbon Sequestration Dynamics in Urban-Adjacent Forests: A 50-Year Analysis A. A. Vais; P. V. Mikhaylov; V. V. Popova; A. G. Nepovinnykh; V. N. Nemich; A. A. Andronova; S. K. Mamedova
Civil Engineering Journal Vol 9, No 9 (2023): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2023-09-09-08

Abstract

Achieving carbon neutrality is crucial for urban ecosystems. Forests growing near cities largely determine the state of the environment in urban areas. The aim of the present research is to assess the carbon productivity dynamics in forests near Krasnoyarsk (a large industrial center) over a 50-year period in terms of carbon sequestration and conservation. The study was based on forest inventory conducted in Karaul'noe Forestry in 1972, 1982, and 2002 and forest inventory covering six forest compartments in 2022. The forest covers 3980 ha and consists of 52 forest compartments. The analysis was based on the assessment of carbon productivity dynamics and followed four levels of principles: forestry, structure, forest compartment, and forest stand. The research was based on forest fund dynamics, analyzing methods, long-term forest inventory, assessing carbon stock, and growing stock dynamics. Pine is the dominant forest-forming species that absorbs the most carbon in the study area. Pine is long-lived, covers a vast area, and has the highest carbon sequestration potential. At the forest structure level, the predominant carbon pools are mid-late successional and late successional stands dominated by pine, birch, and aspen. Forest compartment-level analysis revealed three trends in carbon sequestration: carbon balance, a decrease in carbon sequestration, and an increase in carbon sequestration. Notably, the prevailing trend is determined by changes in carbon sequestration by dominant forest-forming species (pine). Forest stand-level analysis showed that stands have become more and more uneven-aged. About 65% of total carbon stock is concentrated in mid successional, mid-late successional and late-successional stands, and 35% in young stands. The carbon sequestration rate decreases in forests with age. However, pine forests increase biological productivity and continue to successfully sequester carbon. Deciduous forests have lost their carbon sequestration potential, and the area they occupy is currently decreasing in the study area. The development of the young generation in pine stands suggests that the carbon sequestration potential in forests growing near the city will not decrease and may even increase due to climate change. Doi: 10.28991/CEJ-2023-09-09-08 Full Text: PDF

Page 2 of 2 | Total Record : 17

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