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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 1,848 Documents
 180   181   182   183   184 
BIM-Based Integrated Model for Project Cost Estimation: A Case Study for Concrete Elements Elsheikh, Asser; Saqr, Abdullah; Motawa, Ibrahim
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-021

Abstract

Construction projects often struggle to align design models, cost estimates, and scheduling processes. To address this challenge, this study presents an integrated 5D BIM model that automates cost and schedule estimation by linking 3D BIM components to a structured database of historical productivity and activity data. A unique coding system connects each BIM object to its corresponding construction tasks, enabling automatic generation of resource-loaded schedules with associated durations, costs, and crews based on the selected construction method. The workflow integrates Autodesk Revit, Navisworks, a relational (SQL) database, and Primavera P6 to achieve seamless interoperability across design, estimating, and scheduling tools. The model is validated through a case study of a six-story reinforced concrete building. Findings show that the approach significantly improves estimation, accuracy, and efficiency. Predicted costs closely match actual values, thereby reducing dispersion among estimates. The automated process minimizes manual data handling while keeping cost and schedule outputs synchronized. Novel contributions include the incorporation of detailed historical productivity data, construction method alternatives, and structured cost/activity records into a unified framework, representing a methodological advance in 5D BIM that bridges the design, estimating, and scheduling domains for more reliable and automated project planning.
Sizing Optimization of Trusses Using Elitist Stepped Distribution Algorithm Türkezer, Mehmet; Altun, Murat; Pekcan, Onur; Hasançebi, Oğuzhan
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-05

Abstract

This study investigates the efficiency of the recently developed Elitist Stepped Distribution Algorithm (ESDA) as a metaheuristic framework for truss sizing optimization. ESDA builds upon the Cross-Entropy Method by introducing an elitist stepped sampling strategy that improves the balance between exploration and exploitation during the search process. To evaluate its effectiveness, ESDA is applied to a comprehensive test suite comprising seven benchmark truss optimization problems that cover a wide range of sizes, design variables, loading conditions, and constraint types. In all cases, the objective is to minimize structural weight while satisfying stress, displacement, and stability requirements. Numerical experiments are conducted with the proposed method, and the results are compared with those algorithms reported in the literature. The findings show that ESDA attains new best or near-best solutions for large-scale problems such as the 117-bar cantilever, 130-bar transmission tower, 354-bar dome, and 942-bar tower trusses, while also producing competitive results for the 25-bar, 72-bar, and 200-bar structures with relatively modest computational effort. The novelty of this work lies in demonstrating the robustness, efficiency, and scalability of ESDA across diverse benchmarks, highlighting its potential for future structural optimization applications.
Predicting the UCS of Industrial Byproduct-Based CLSM Using Machine Learning and Experiments Singh, Chandan K.; Kumar, Divesh R.; Lini Dev, K.; Wipulanusat, Warit
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-04

Abstract

This study investigated the development of sustainable Controlled Low Strength Material (CLSM) using industrial by-products pond ash, fly ash, and red mud as alternatives to conventional concrete constituents. This research employs a dual methodology: comprehensive experimental testing aligned with ASTM standards and the implementation of advanced machine learning (ML) techniques to predict the unconfined compressive strength (UCS) of CLSM mixes. Experimental datasets, generated through the variation of key material and mix design parameters, were utilized to train ensemble-based supervised ML models, including ADAboost, XGBoost, gradient boosting machine (GBM), and random forest (RF). A comparative performance evaluation was conducted, and the XGBoost model emerged as the most accurate predictor, achieving R² values of 0.969 for training and 0.933 for testing, surpassing GBM, ADAboost, and RF across multiple performance indicators. The optimal model was subsequently embedded into a graphical user interface (GUI) for UCS prediction. A sensitivity analysis based on the XGBoost model revealed that cement, water, and curing age were the most influential parameters affecting UCS, with cement exhibiting the highest impact value of 0.86 and a relative contribution of 19%. These findings emphasize the significance of these variables in strength development and mix optimization. The integration of experimental validation with predictive modeling not only advances the understanding of CLSM behavior but also underscores the utility of ML in the formulation of sustainable construction materials. This research supports the beneficial reuse of industrial waste, aligns with environmental sustainability goals, and provides an efficient and reliable tool for CLSM mix design.
The Role of Recycled Plastic Bottles in Enhancing Asphalt Longevity Al-Tuwayyij, Husham; Al-Mukaram, Noorance; Ali, Atheer M.
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-09

Abstract

Producing “green” pavement is important in decreasing the negative effects of plastic on the environment and ensuring sustainable resource management. Because many worldwide strategies are aimed at reducing the use of plastic, this work studies a recycled polymer concrete modified by a defined amount of recycled plastic waste in asphalt. The specimens were prepared with a maximum optimal asphalt content using ±0.5% of the optimum level. The logic indicated that 11% plastic waste can be used as an alternative to the coarse aggregate. Experimental tests were carried out to examine moisture damage, short- and long-term aging, and compressive strength (rutting resistance). The measured properties were ITS, resilient modulus, and permanent deformation of the first load cycle and after 1200 load cycles using the PRLS device. In aging experiments, the resilient modulus was found to increase by 118% during the first cycle and by 40% after 1200 cycles. The decrease in permanent deformation was 40% and 48.5% after the first load cycle and after 1200 cycles, respectively. The results obtained in the moisture susceptibility test were within the required limit. Finally, the compressive strength of samples with asphalt content of 4.0%, 4.5%, and 5% was found to be 3660, 4120, and 2900 kPa, respectively. This achievement indicates the advantages of utilizing plastic waste in road construction to develop sustainable asphalt concrete with improved mechanical properties and reduced environmental impact, especially in hot climates such as Iraq, where it would be beneficial for rutting-sensitive roads.
Bio-Based Modification of Natural Rubber-Modified Asphalt Using Hard Resin from Yang Sinthorn, Poramin; Tirapat, Supakorn; Katekaew, Somporn; Wongsa, Ampol; Posi, Patcharapol; Thongchom, Chanachai; Chindaprasirt, Prinya
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-018

Abstract

This study investigates the potential of hard resin derived from the Yang tree (HY), a renewable bio-based byproduct, as a performance-enhancing additive in natural rubber-modified asphalt (NRMA). HY-modified binders (HYMA) containing 3%, 7%, and 15% HY by weight were evaluated through a multi-scale experimental program, including physical, rheological, thermal, chemical, and mechanical tests. Standard binder characterizations (penetration, ductility, softening point, viscosity), spectroscopic analyses (FT-IR, NMR), microstructural observations (ESEM, XRD), thermal profiling (DSC), and performance assessments (DSR, Marshall) were conducted. The results demonstrated that HY improved binder properties at optimal concentration by introducing additional hydrocarbon structures without chemical cross-linking. HYMA3 achieved the most favorable balance of stiffness, flexibility, and compaction efficiency, whereas higher HY contents (≥7%) impaired structural integrity and deformation resistance. Microstructural and thermal evidence confirmed surface modifications and altered thermal transitions, which influenced viscoelastic response. These findings provide new insights into bio-resin–asphalt interactions and establish the viability of HY as a sustainable alternative to synthetic polymer modifiers. Beyond performance improvement, HY promotes circular construction by transforming agricultural byproducts into functional pavement materials, supporting the development of climate-adaptive infrastructure.
The Effect of Fiberglass Paint Coating on the Shear and Flexural Strength of Concrete Blocks Juliafad, Eka; Restu, Lisyana Junelin; Yusmar, Fajri; Sandra, Nevy; Putra, Rusnardi Rahmat
Civil Engineering Journal Vol. 11 No. 12 (2025): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-12-04

Abstract

This study uses an experimental method to investigate the behavior of concrete blocks coated with fiber paint, focusing on their shear and flexural strength, ductility, stiffness, and energy dissipation to enhance their mechanical performance. The fiber paint coatings used in this study were applied in different thicknesses, namely 1 mm, 2 mm, and 3 mm. The results show that a 3 mm coating provided the highest improvement, with shear and flexural strengths increasing by 47.36% and 66.06%, respectively. Flexural ductility improved by up to 32%, while stiffness increased by 12% in flexure and 13% in shear. Energy dissipation also showed significant enhancement; total flexural energy increased from 1.38 kNmm to 10.76 kNmm at 3 mm, and shear energy dissipation reached 50.72 kNmm at 3 mm. These results confirm that fiber paint can enhance the shear and flexural strength, ductility, stiffness, and energy dissipation of concrete blocks. This study introduces fiber paint as a practical reinforcement method for concrete block materials, offering a simple, easy-to-apply, and cost-effective alternative that improves both mechanical and aesthetic performance.
Behavior of Deep Beams with Different Proportions of Recycled Plastic Type HDPE Instead of Coarse Aggregate Abawi, Ahmed M.; Salih, Oday Asal
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-03

Abstract

One of the most appealing strategies in the ongoing effort to lessen humans' impact on the environment is using waste plastic as coarse particles in concrete. This innovative approach addresses the pressing issue of mounting plastic waste and aims to diminish the adverse effects of traditional building materials, such as natural aggregates, on the environment. Plastic waste as coarse aggregates exemplifies a professional dedication to creating a resilient infrastructure that mitigates environmental harm and contributes to a greener future for future generations. Eight deep beams were cast with sustainable concrete that was made from two mixtures: one in normal strength (C30) and the other in high-strength concrete (HSC) (1% Hyperplast PC200 of cement) that included HDPE plastic, which was taken from fruit boxes that had been crushed and used in 10, 20, and 30 percentage volumetric proportions as a substitute for coarse aggregate. The two still intact have no HDPE replacement and serve as each deep beam's reference deep beam. Shear failure and ductility in the second group were slightly lower than 2% compared to the reference beam for B30. It can be argued that while the replacement has positive environmental impacts, the 23.5% loss in strength is unwanted, while the 2% decline in ductility is acceptable. While maintaining a competent structural flexural behavior, the first group demonstrated an increase in shear failure by the replacement rate (20%, 30%), and the 10% replacement rate dropped by a tiny percentage (1.25%) in comparison to the reference specimen.
Numerical Analysis on Fatigue Performance in Fillet Weld Roots of Steel Bridge Bearings Mao, Jiahao; Jiang, Feng; Hirohata, Mikihito; Qin, Yanyue
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-020

Abstract

Fillet weld roots near bridge supports are critical fatigue-prone details in steel bridges, particularly under high stress concentration. Fatigue cracks at these locations tend to be initiated internally, where detection and repair remain challenging with current techniques. Fatigue performance improvements are explored from the perspectives of structural design and epoxy insertion. Five actual bridges in the USA, China, and Japan were analyzed using a hybrid finite element modeling approach, employing low-precision girder models for load distribution and high-precision local support models with an introduced notch for Effective Notch Stress (ENS) evaluation. Both actual bridge case studies and numerical parametric analyses were conducted. Results indicate that increasing weld size effectively reduces ENS, while sole plate thickness has a limited effect. Bolts play a pivotal role in limiting relative displacement between the bottom flange and the sole plate, though their constraint range is localized. To address the limited effectiveness of structural adjustments, adhesive filling was introduced in areas beyond the bolt constraint range. Bonding-assisted welding with epoxy insertion achieved up to a 56% reduction in ENS and significantly improved fatigue performance. The findings confirm the potential of bonding-assisted welding for improving the durability of fillet weld roots in steel bridge supports and provide practical solutions to the difficult-to-detect root fatigue cracks.
Evaluation of Flood Inundation Image Detection Performance Using Deep Learning Soebroto, Arief A.; Limantara, Lily M.; Suhartanto, Ery; Moh. Sholichin; Ramdani, Fatwa; Rachmawati, Turniningtyas A.
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-08

Abstract

Floods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems.
Groundwater Quality and Irrigation Suitability Assessment Using Geochemical and GIS-Based Approaches in Arid Regions Radhi, Noor A.; Al-Madhhachi, Abdul-Sahib T.; Sachit , Dawood E.
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-06

Abstract

In arid and semi-arid climates, such as Iraq's Salah Al-Din Governorate, the availability of surface water is much lower than demand, so groundwater becomes a vital resource. Groundwater is one of the basic needs for agricultural irrigation, and therefore this study presents a suitable groundwater suitability assessment for agricultural irrigation based on a comprehensive assessment of groundwater geochemical properties and spatial distribution using the kriging technique within Geographic Information Systems (GIS). Key water quality parameters, including EC, TDS, pH, Cl⁻, Na⁺, K⁺, NO₃⁻, HCO₃⁻, CO₃²⁻, SO₄²⁻, Ca²⁺, and Mg²⁺, were determined in a total of 51 wells across the study area. In addition, two wells located in the Al-Alam District of Salah Al-Din Governorate were remeasured in 2025 to assess changes in water levels. These measurements were compared to the static water levels recorded in 2014 for one well and in 2008 for the other. To determine irrigation suitability, the Water Quality Index, Sodium Adsorption Ratio, Residual Sodium Carbonate, and Total Hardness were calculated and analyzed. Groundwater quality was spatially variable, but several areas exceeded the FAO limits for safe agricultural use at all groundwater depths considered owing to salinity, sodicity, and anthropogenic contamination. Spatial mapping using GIS identified the risk zones and assisted in recommending appropriate management practices for sustainable groundwater development. Such findings emphasize the importance of regular monitoring together with appropriate irrigation management and remediation measures to reduce groundwater degradation and maintain agricultural development in Salah Al-Din Governorate.

Page 182 of 185 | Total Record : 1848

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