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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 1,972 Documents
 194   195   196   197   198 
Bearing Capacity Enhancement of Hexagonal Skirted Footings: Numerical, Regression, and ANN-Based Prediction Ahmed S. Jawad; Hayder A. Mahdi; Alaa H. Al-Zuhairi; Ayad Al-Rumaithi
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-06

Abstract

This paper presents a comprehensive numerical analysis of the improvement in bearing capacity and settlement performance of hexagonal shallow footings with inclined skirts. Various numerical analyses were conducted using PLAXIS 3D to investigate the influence of skirt length-to-footing width (L/B) ratios and skirt inclination angles (θ) on hexagonal footings in loose sand. The models showed very good agreement with experimental data reported in previous studies, with an R² value of 0.996 and a maximum error of less than 4.31%. It was concluded that the inclusion of inclined skirts has a positive effect on bearing capacity, increasing it by up to approximately 2.97 times compared to non-inclined configurations, while significantly reducing settlement. In addition to numerical simulations, an empirical formula for bearing capacity and settlement was developed using multiple regression based on geometric and inclination parameters. The model demonstrated a good fit (R² = 0.993). Furthermore, an Artificial Neural Network (ANN) model with a 4-10-10-1 architecture was proposed to predict bearing capacity using normalized input parameters, including skirt depth, inclination angle, stress, and settlement ratio. During training, validation, and testing, R² values greater than 0.998 were achieved, indicating a high level of accuracy with low prediction error. These findings highlight the importance of skirt inclination in enhancing foundation design, providing an efficient and cost-effective approach to increase the safety factor of foundations constructed on weak soils without the need for additional structural elements such as panels or strips.
Waterproofing Admixture and Aloe Vera Biopolymer Gel in Concrete: Microstructure, Durability and Structural Validation Marlon Cubas; Pedro Patazca; Robert Suclupe; Luis Villegas; Omar Coronado; Oscar Alvarado; Javier Guerrero; Brayan Perleche
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-09

Abstract

Concrete durability in aggressive environments is often limited by chloride ingress, carbonation, and sulfate attack, which compromise structural integrity and increase maintenance costs. This study examines the combined effects of an integral waterproofing admixture (Sika®-1, 3– 4% cement weight) and Aloe vera biopolymer (1–2% cement weight) on mechanical performance, durability, and microstructural characteristics of conventional concrete. Four mixtures were produced: a control (P1) and three hybrid formulations (P2: 4%S1+1%AV; P3: 3.5%S1+1.5%AV; P4: 3%S1+2%AV), subjected to fresh state testing, strength development at 7, 14, and 28 days, and durability assessment including water permeability, chloride penetration, sulfate resistance, carbonation depth, ultrasonic pulse velocity, and surface abrasion through 56 days, alongside X-ray diffraction, Fourier-transform infrared spectroscopy, and scanning electron microscopy analysis. The optimal mixture (P4) achieved 28.77 MPa compressive strength, reduced water permeability to 0.00420 cm/s, lowered chloride penetration to 138.38 Coulombs, and minimized carbonation depth to 0.44 mm, with microstructural analysis revealing enhanced C-S-H gel densification and refined porosity. Pilot-scale reinforced concrete frames fabricated with P4 exhibited 9.6% lower maximum strain, confirming improved structural stiffness and durability. Techno-economic evaluation yielded an index of 1.104, demonstrating economic viability despite an 11.2% material cost increase. These results support the use of the hybrid admixture system as a sustainable option for extending concrete service life in marine, industrial, and tropical environments.
A BIM-Integrated Stage-Gated Framework for Mitigating Strategic Design Errors in Infrastructure Projects Alaa T. Alisawi; Ruqayah F. Alrubaye; Philip E. F. Collins
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-012

Abstract

Design errors remain a persistent challenge in infrastructure delivery, particularly when strategic errors introduced during early design stages propagate into later project phases. This study develops a Building Information Modeling (BIM)-integrated stage-gated framework to mitigate strategic design errors across the infrastructure design lifecycle. The proposed approach embeds interdisciplinary coordination, iterative model federation, and structured verification checkpoints throughout conceptual, preliminary, and detailed design phases. The framework was implemented through a BIM workflow using Civil 3D, Revit, and Navisworks and applied to the Al Najaf Airport Road project in Iraq as a case study. A standards-based geometric and functional assessment was conducted to evaluate both the baseline design and the redesigned solution developed through the proposed framework. The analysis revealed that the baseline design satisfied only 39% of the evaluated design criteria, indicating significant geometric and operational deficiencies. After applying the BIM-integrated framework, the redesigned scheme achieved full compliance with the evaluated standards while eliminating previously undetected coordination conflicts. Model-based analyses also enabled targeted traffic and drainage assessments, helping identify and mitigate potential risks such as flooding susceptibility and unsafe junction configurations prior to construction. The findings demonstrate that early and continuous BIM integration can function as a proactive design assurance and risk management mechanism rather than a late-stage coordination tool. The proposed framework contributes a structured methodology for preventing strategic design errors and improving reliability in BIM-enabled infrastructure projects.
Comparative Assessment of Soil Salinity Using Sentinel-2 and Landsat-7 Remote Sensing Data Mohamed A. Elshewy; Mohamed Freeshah; Mostafa H. A. Mohamed; Mahmoud M. E. Gad; Mervat M. Refaat
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-021

Abstract

This study evaluates the performance of the Sentinel-2 multi-spectral instrument (MSI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) for soil salinity mapping across contrasting agroecosystems in Egypt, with particular emphasis on subsurface salinity conditions (>0.5 m). A multi-stage calibration framework was implemented, in which historical Landsat-5 imagery (1995) was first integrated with field-measured electrical conductivity (EC) data to establish a spectral baseline. This baseline was subsequently applied to Sentinel-2 and Landsat-7 imagery acquired in 2015 and validated using in-situ total dissolved solids (TDS) measurements. Among the evaluated spectral indices, Salinity Index 5 (SI5) demonstrated the strongest relationship with field data and was selected for salinity mapping. Comparative analysis revealed that Sentinel-2 significantly outperforms Landsat-7, achieving a higher predictive accuracy (R² = 0.89) compared to Landsat-7 (R² = 0.72), primarily due to its finer spatial resolution (10 m) and reduced mixed-pixel effects. In addition, the application of second-degree polynomial regression substantially improved model performance relative to linear approaches, confirming the non-linear nature of soil salinity–spectral relationships. The results further indicate that surface spectral indices can provide meaningful estimates of subsurface salinity under specific environmental conditions. Overall, the integration of multi-temporal satellite data, robust spectral indices, and non-linear modeling provides an effective framework for soil salinity assessment in arid environments. This approach enhances the reliability of remote sensing-based monitoring and supports sustainable land management in salinity-affected regions.
CFD-Based PSO Optimization of Bamboo Roof Trusses Under Wind Loading Nurwin Adam G. Muhammad; Jerson N. Orejudos; Mary Joanne C. Aniñon; Lessandro Estelito O. Garciano
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-023

Abstract

Bamboo roof trusses are promising for low-carbon housing, but their performance under strong wind is highly influenced by roof geometry. This study developed a coupled computational fluid dynamics (CFD) and particle swarm optimization (PSO) framework to optimize a bamboo Howe roof truss under extreme wind loading. The objective was to reduce the maximum member utilization by finding a roof-truss geometry that responds efficiently to geometry dependent wind effects. For each candidate geometry, ANSYS SpaceClaim and ANSYS Fluent were used to update the roof profile and compute the wind-induced force resultants acting on the roof surfaces. These force resultants, together with roof and ceiling dead loads, were then applied to a MATLAB two-dimensional frame model to calculate member forces, deflections, and utilization ratios. The PSO run converged after 660 objective-function evaluations over 22 iterations using 30 particles per iteration. The optimized truss had a ridge height of hr = 2.079 m, r1 = 0.324, and r2 = 0.487, giving a maximum member utilization of 0.116, small deflection, and a truss volume of approximately 0.05 m³. Compared with the best solution in the first iteration, the optimized design reduced maximum utilization by 37%. The main contribution of this study is the integration of CFD derived wind loading with PSO-based bamboo truss optimization, allowing both wind demand and structural response to be updated during the search process.
Artificial Neural Network and Reliability-Based Design of Concrete Beams Reinforced by FRP Bars Hau Tran; Trung Nguyen-Thoi; Quang-Thien-Buu Nguyen
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-05

Abstract

FRP bars have been utilized widely to replace steel bars in concrete beams due to their excellent corrosion resistance. Therefore, this paper aims to propose an efficient procedure based on artificial neural network (ANN) and reliability analysis to predict the moment capacity, the failure modes, and the resistance reduction factor for the design of concrete beams reinforced by FRP bars. In particular, 200 FRP RC beams are collected to train and verify the ANN model. In addition, a source code based on the Monte Carlo method is developed in MATLAB for the reliability analysis. The ANN model and the Matlab code are integrated to determine the failure probability, the reliability index, and the resistance reduction factor of FRP RC beams by rigorously considering the uncertainty of numerous variables. According to the findings of this study, ANN can be applied to predict the ultimate moment of FRP RC beams well since the mean and CoV of the model error are only 0.98 and 0.12, respectively, which are better than those obtained from ACI 440.1R. Furthermore, the resistance reduction factors for the design of FRP RC beams by ANN can be taken as 0.65 corresponding to the target reliability index of 4.0.
Rheological Performance of Asphalt Mastics Incorporating Shale and Pumice as Alternative Mineral Fillers Suwaphit Chamwon; Multazam Hutabarat; Preeda Chaturabong
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-020

Abstract

This study investigates the hypothesis that mineral fillers with distinct surface characteristics, mineralogical compositions, and morphologies exhibit different reinforcement mechanisms in asphalt mastics. Shale and pumice were evaluated as alternative mineral fillers and compared with conventional granite and limestone at 20% and 30% filler-to-asphalt (F/A) ratios by volume. Filler characterization included X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), specific surface area (SSA), and hydrophilicity coefficient (HC) measurements. Rheological characterization was performed using dynamic shear rheometer, including temperature sweep, frequency sweep master curves, multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), and Glover–Rowe (G–R) analyses. Pumice, dominated by amorphous volcanic glass with the highest SSA (59.18 m²/g), exhibited rutting-dominant modification with the highest complex modulus enhancement (7.3–9.4 times at 30% F/A) and lowest non-recoverable creep compliance. Shale, composed primarily of quartz and kaolinite with layered morphology and moderate SSA (43.00 m²/g), demonstrated balanced rheological response and achieved the longest fatigue life (Nf,5% = 45,200 cycles at 20% F/A). These findings demonstrate that filler-specific reinforcement mechanisms are governed by mineralogical composition and morphology, supporting performance-based filler selection tailored to climatic and loading conditions.
Impact of Cooling Methods on the Valorisation of Calcined Dam Sediments in Self-Compacting Concrete Ahmed Sweiti; Rabah Chaid; El-Hadj Kadri
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-03

Abstract

Dam sedimentation poses critical environmental and operational challenges worldwide, requiring sustainable valorisation strategies. This study investigates how post-calcination cooling protocols influence the pozzolanic performance of Ksob dam sediments (Algeria) as a partial cement replacement in self-compacting concrete (SCC). Raw sediments were calcined at 750 °C for 5 h and subjected to three cooling methods: water quenching (WQCS), air cooling (ACCS), and slow furnace cooling (SCCS). Ten SCC formulations were prepared with 10%, 15%, and 20% cement substitution rates. Despite the reduced binder content, all mixtures maintained self-compacting properties (spread: 700-735 mm; T₅₀₀: 1.06-1.39 s) with moderate superplasticiser adjustment, up to 1.2% of binder mass. WQCS formulations exhibited superior performance: at 10% substitution, compressive strength reached 97% of the control at 180 days, while water absorption and permeable porosity decreased relative to the control by 7.1% and 1.9%, respectively. TGA/DSC analysis attributed these gains to enhanced pozzolanic C-S-H formation. These findings demonstrate that cooling kinetics critically govern the mineralogical transformation and reactivity of calcined sediments. Water quenching proved optimal for producing high-performance, eco-efficient SCC, offering a viable pathway for large-scale dam sediment valorisation while lowering the cement industry’s carbon footprint.
Predicting Temperatures in an Extreme Climatic Environment Using Hybrid Neural Networks: Evaluating Noise Robustness Ali W. Alattabi; Salah L. Zubaidi; Hussein Al-Bugharbee; Hussein Mohammed Ridha; Mawada Abdellatif; Hassimi Abu Hasan
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-017

Abstract

Predicting maximum temperatures is crucial across many fields and industries, including medicine, agriculture, energy, and climate research. Researchers have not treated the prediction of maximum temperatures under severe artificial data disturbance in much detail. So, it has not yet been understood. This research aims to integrate an artificial neural network (ANN) with the Guaranteed Convergence Arithmetic Operation Algorithm (GCAOA) to forecast monthly maximum temperatures while ensuring robustness to noise. Univariate data from Al-Hai City over 12 years were employed to build and assess the model. The performance of GCAOA was examined and compared with that of the two hybrid ANNs, the random forest, and the XGBoost models. Across various input scenarios, the results reveal that these three hybrid models achieved very good forecast performance compared with random forests and XGBoost. The GCAOA-ANN (swarm size of 20 and lag2) achieves the best forecast performance among the hybrid algorithms across different statistical fitness measures with a coefficient of determination, Nash-Sutcliffe coefficient, and root mean squared error of 0.972, 0.969, and 1.7354°C, respectively. The performance of the hybrid ANN models was further investigated under noise, and the results showed the superiority of the GCAOA-ANN model.
High-Resolution Assessment of Wastewater Heat Recovery Potential for Urban Decarbonization Emil Tsanov; Galina Dimova; Ivelina Hinova; Viden Radovanov; Valentina Dimova
Civil Engineering Journal Vol. 12 No. 5 (2026): May
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-05-01

Abstract

This study assesses the technical potential for wastewater heat recovery in Sofia and its contribution to domestic hot water supply and greenhouse gas reduction. Heat extraction locations were identified using temperature and flow data from the sewer network and the municipal wastewater treatment plant (WWTP) Kubratovo. Temperature thresholds were defined to ensure stable biological treatment ( ≥ 10°C influent) and environmental protection ( ≥ 5°C effluent). Four scenarios were analyzed, considering heat recovery at the WWTP inlet and outlet with ΔT = 2–4 K. The heat recovery potential is evaluated using 15-minute temperature and flow data by applying scenario-specific temperature thresholds, enabling a dynamic assessment that captures real variations in both flow and temperature and explicitly accounts for system shutdown periods. Heat recovery at the WWTP effluent provides higher and more stable yields than at the influent. The potential ranges from 238,536–380,314 MWh/year at the inlet and 264,828–529,705 MWh/year at the outlet. Under the most favorable scenario (effluent, ΔT = 4 K), the recovered heat can supply domestic hot water to over 76,000 households, reducing emissions by more than 200,000 t CO₂/year when replacing natural gas.

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