cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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 24 Documents
 1   2   3 
Search results for , issue "Vol. 12 No. 4 (2026): April" : 24 Documents clear
Influence of Micro Silica and Portland Cement on Geopolymer Concrete Containing Recycled Asphaltic Concrete Aggregate Athika Wongkvanklom; Patcharapol Posi; Puridet Kotaniwong; Phatsarapha Chakamnan; Piyawat Foytong; Sumrerng Rukzon; Prinya Chindaprasirt
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-014

Abstract

In this paper, the influence of micro silica (MS) and Portland cement (PC) on geopolymer concrete containing recycled asphaltic concrete aggregate was examined. The basic mix consisted of high-calcium fly ash (HFA), river sand, crushed limestone, recycled asphaltic concrete aggregate (RACCA), sodium silicate, and sodium hydroxide. Coarse aggregate was replaced with RACCA at 0, 20, and 40% by weight. MS and PC were used as hybrid additives to partially replace HFA. The tested MS-to-PC ratios were 0:10, 2.5:7.5, 5:5, and 10:0 by weight. Values were obtained for slump flow of 69-76 cm, 28 day compressive strength of 22.3-63.9 MPa, flexural strength of 2.20-4.70 MPa, shear bond strength of 6.45-19.90 MPa, and bond strength between geopolymer concrete and rebar of 4.95-7.30 MPa. The mix with 20% RACCA and an MS-to-PC ratio of 2.5:7.5 hybrid additive produced the best performance with values for compressive strength of 57.4 MPa, flexural strength of 4.30 MPa, slant shear bond strength of 16.69 MPa, and bond strength to rebar of 6.78 MPa. Thus, based on the results, RACCA could be used to make high-strength concrete according to the ACI 363.2R-11 standard, using MS and PC as enhancing materials.
Mineralogical, Chemical, and Geotechnical Characterization of Natural Clay for Ceramic Applications Abbou Mohammed; Boumelik Zoheir; Semcha Abdelaziz
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-012

Abstract

In the framework, the sustainable local development of the Adrar region is one of the largest in the Algerian Sahara. The Algerian government has launched a search for useful local substances to cover the need for building materials in the construction sector. However, the Algerian Sahara has a variety of mineral resources, including clays. This work aims to characterize and identify a natural Algerian clay from the Reggane basin (Paleozoic sedimentary basin) in southwestern Algeria. This is for use in the manufacture of ceramic products. For this, numerous analyses were carried out using techniques such as X-ray Diffraction (XRD) to determine the different crystalline mineral phases, X-ray Fluorescence (XRF) to identify the elemental composition, and Infrared Spectroscopy (FTIR) to study the molecular structure along with the geotechnical identification in order to better understand the main properties of this clay. The findings indicated that Reggane clay is silty and highly plastic (21.94-31.7). It contains a mixture of illite, kaolinite, and quartz, in very significant proportions, as well as hematite, orthoclase, and palygorskite. Furthermore, elemental chemical analyses were conducted, and the results showed that the main constituents of this clay are SiO₂ (58.19%-61.71%), Al₂O₃ (13.32%-13.50%), and Fe₂O₃ (6.13%-6.40%). These findings could eventually be used to target applications of this clay in the production of local fired materials.
Adaptive Real-Time Strain-Rate Control in CRS Consolidation Testing Using SARSA Reinforcement Learning Muhammad Fadhl 'Abbas; Hasbullah Nawir; Dimitri Mahayana; Erza Rismantojo; Dayu Apoji; M. Alifsyah Putra Nasution; Targhib Ibrahim
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-023

Abstract

This study presents a reinforcement-learning framework for real-time strain-rate control in Constant Rate of Strain (CRS) consolidation testing to hasten the testing process using the SARSA algorithm. The controller adaptively adjusts deformation rate based on evolving pore-pressure ratio, with a reward strategy designed to maintain an average pore-pressure ratio near 30% to ensure partially drained conditions consistent with CRS theory. Two normally consolidated clays with contrasting compressibility were modeled numerically using a 1-D CRS consolidation model to evaluate learning and testing performance. The results show that the SARSA agent autonomously learns soil-specific strain-rate policies and maintains smooth effective stress trajectories and stable pore-pressure ratio responses. Test duration reductions of 60-75% were achieved depending on soil type. The interpreted compression index (Cc) remains consistent with the baseline CRS values, confirming that reinforcement-learning-based strain-rate control can accelerate testing without compromising data integrity. The study demonstrates the feasibility of reinforcement learning for CRS testing and highlights practical potential for soil-responsive, adaptive strain-rate control. Current limitations include simulation-based evaluation, discretized action selection, and the need for multiple runs to achieve optimal convergence.
Mechanical and Thermal Properties of Geopolymerized LECA Concrete Hebah M. Al-Jabali; Abd Al-Kader A. Al Sayed; Mohamed Fahmy; Amr A. Nada; Amr Abdelkhalik
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-021

Abstract

This study aims to enhance the mechanical and thermal performance of lightweight expanded clay aggregate (LECA) concrete through geopolymerization using iron oxide (Fe₂O₃) and polyethylene glycol 400 (PEG400), combined with surface treatment of LECA aggregates. An experimental program was conducted to evaluate workability, density, water absorption, compressive strength, splitting tensile strength, and bulk electrical resistivity (BER). Various mixtures with different proportions of Fe₂O₃ and PEG400 were prepared with and without aggregate surface treatment. The findings indicate that surface treatment significantly improves the interfacial transition zone, resulting in enhanced overall performance. The optimal mix (treated LECA with 3% PEG400 and 20% Fe₂O₃) achieved a compressive strength of 45 MPa and a splitting tensile strength of 4.0 MPa, representing increases of over 70% compared to the control mix. Additionally, water absorption decreased by 35.6%, while BER increased by 127%, reflecting improved durability and reduced permeability. Workability was also enhanced, with up to a 100% increase in slump without compromising strength. The novelty of this study lies in the synergistic integration of treated LECA, PEG400, and iron oxide within a geopolymer matrix to produce a high-performance, durable, and thermally efficient lightweight concrete. This approach offers a sustainable solution for advanced construction applications.
Energy-Based Evaluation of Brittleness in Moderately Weathered Rocks Under Triaxial Compression Lianghong Lv; Enze Yin; Xiaoyu Liu; Ke Wu
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-08

Abstract

Moderately weathered rock masses are widely encountered in deep and long tunnels, where excavation-induced unloading–reloading and stress concentration strongly affect failure behavior, stability, and support design. To clarify their brittleness from an energy perspective, this study develops a unified three-dimensional numerical framework for the triaxial compression analysis of five typical moderately weathered rocks, namely granite, basalt, limestone, shale, and sandstone, with the Jinmen Tunnel of the Longchuan–Huaiji Expressway as the engineering background. A coupled damage–plasticity constitutive model is adopted, and its parameters are calibrated using laboratory triaxial test data. Model reliability is verified by the close agreement between simulated and measured stress–strain curves and failure patterns of moderately weathered granite. The external work is decomposed into total, elastic, and dissipated energy densities, and a normalized dissipated energy is introduced to describe damage evolution. Results show that the energy evolution is strongly lithology-dependent: granite and shale exhibit a clear transition to post-peak dissipation-dominated behavior, whereas basalt, limestone, and sandstone remain mainly controlled by elastic energy. Increasing confining pressure from 5 to 10 MPa expands the dissipation zone and reduces the energy-density brittleness index BED, indicating a ductilizing effect. The novelty lies in the unified energy-based framework, the normalized dissipated energy, and the newly proposed BED for brittleness evaluation.
Investigation on the Mechanical Behavior of Passively Confined Cementitious Treated Sand Laras Laila Lestari; Bambang Piscesa; Priyo Suprobo; Yudhi Lastiasih
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-03

Abstract

This study aims to develop a practical and accessible approach for evaluating the mechanical behavior of Cementitious Treated Sand (CTS) under passive confinement using Glass Fiber Reinforced Polymer (GFRP) wraps. A method utilizing three GFRP layer configurations was applied to investigate the confinement effect and assess the role of confining stiffness. Path-dependency was analyzed through derived confining pressure rates, and Mohr-Coulomb failure analysis was used to determine shear-strength parameters. Analysis of plastic volumetric behavior revealed that after an initial elastic state, the material dilates upon yielding—activating the confinement mechanism—before recompacting under sufficient confining pressure due to pore structure collapse. Results indicate that the proposed novel constitutive model successfully predicts both axial and lateral stress-strain responses. It accurately represents the nonlinear stress-strain relationship, the transition in volumetric behavior, and the interaction between axial and lateral strains through the proposed dilation formulation. The model incorporates a plastic dilation rate model to capture the dilation-to-compaction transition and demonstrates excellent agreement with experimental results across all confinement levels. This framework provides a reliable analytical tool for designing soil stabilization schemes using passive confinement, offering engineers a practical alternative to conventional geotechnical analysis while enhancing reproducibility, sustainability, and applicability across diverse construction projects.
Bridge Maintenance Prioritization and Condition Rating Based on Fermatean Fuzzy AHP Approach Salsabeel Sahib Jafar; Sawsan Rasheed Mohammed
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-011

Abstract

Bridges are considered critical components of transportation infrastructure and play an integral role in public welfare and economic development. Bridge authorities in Iraq face multiple challenges in maintaining the efficiency and serviceability of the bridge network while developing a maintenance plan within limited budgets. Thus, this study aims to develop a systematic condition assessment methodology as a tool to prioritize maintenance projects and optimize available budgets to enhance the management of bridge networks. For this purpose, the bridge structure is broken down into four components: deck, superstructure, substructure, and accessories, and each component is divided into a number of elements. Bridge maintenance experts were surveyed to assign weights for the identified components and elements using the Fermatean fuzzy Analytic Hierarchy Process (FF-AHP). The weighted averaging approach was then used to aggregate components' condition ratings with expert-determined weights to obtain the overall Bridge Condition Index (BCI) of each bridge. Bridges with the lowest BCI get higher priority for maintenance. The proposed methodology was applied to thirteen bridges in Baghdad to demonstrate its practicality. The results indicate its reliability and capability to evaluate and rank bridges based on their urgency for maintenance. The proposed method would help bridge engineers and policymakers to make informed maintenance investment decisions during the budget allocation process.
Application of XGBoost in Road Maintenance Cost Prediction Dian Setiawan; Leksmono S. Putranto; Endah Murtiana Sari
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-018

Abstract

Road maintenance costs play a critical role in government budgeting, as they represent a recurring expenditure required to sustain transportation infrastructure performance and traffic safety. Accurate cost prediction enables long-term efficiency by ensuring that maintenance budgets are allocated appropriately. This study aims to develop a predictive model for road maintenance cost using the Extreme Gradient Boosting (XGBoost) algorithm, optimized through iterative training to improve prediction accuracy based on deviations between predicted and actual costs. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²), all of which indicate a strong model fit and high predictive reliability. The model was developed using simulated and empirical data from 30 road sections with varying characteristics, incorporating key predictors such as road length, cold mix asphalt, asphalt emulsion, diesel fuel, gasoline, water consumption, working area, asphalt removal volume, and labor requirements. The results demonstrate that the proposed XGBoost-based model can effectively estimate maintenance costs and associated resource requirements. The findings provide practical insights for government agencies in planning material usage and workforce allocation for road maintenance activities.
Evaluating AI-Based Video Analytics for Traffic Engineering: Accuracy, Calibration, and Practical Use Dražen Cvitanić; Biljana Maljković; Sanja Vrdoljak
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-04

Abstract

This paper examines the potential and reliability of AI-based video analytics for solving key traffic engineering problems. The objectives were to compare several commercially available tools for collecting traffic data and, through practical examples, to show that AI-processed data can be used for the development, calibration, and validation of traffic models. Four AI-based video analytics (StreetLogic Pro, DataFromSky, CVEDIA RT Studio, and Camlytics Single) were tested using field video recordings at a signalized intersection on an urban arterial in Split, Croatia. Detection accuracy, usability, and sensitivity to camera placement and recording conditions are analyzed, and selected microscopic parameters (saturation flow rate and control delay) were obtained and compared with values derived from HCM procedures. DataFromSky and CVEDIA RT Studio achieved 97–99% vehicle detection accuracy and provided detailed trajectory data suitable for scientific applications, while StreetLogic Pro achieved 100% accuracy for operational vehicle counting. AI-based estimates of saturation flow rate and control delay differed by less than 1% and 5%, respectively, from traditional field measurements. The main novelty of this research lies in its practical comparison of AI-based video analytics tools combined with a worked example of using AI-derived data to calibrate analytical models, providing practical guidance for researchers and practitioners in traffic engineering.
Dynamic Responses of a Cylindrical Lattice Shell Structure with Explosion Venting Holes under Internal Explosion Shiqi Fu; Xuanneng Gao
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-022

Abstract

This study investigates the dynamic response of cylindrical steel lattice shell structures subjected to internal explosions and evaluates the effectiveness of explosion venting holes in mitigating structural damage. A detailed numerical model was developed using ANSYS/LS-DYNA and validated against experimental results. The comparison shows good agreement in both overpressure and structural strain responses, confirming the reliability of the model. Internal explosions produce complex shock wave reflections and convergence within confined spaces, leading to severe structural responses that differ significantly from those caused by external explosions. Based on the validated model, a systematic parametric analysis was conducted to examine the effects of venting hole arrangement, venting ratio, charge mass, connection stiffness, and rise-to-span ratio. The results show that dome-mounted and evenly distributed venting holes with a venting ratio of approximately 50% provide the most effective mitigation performance. Compared with a fully confined configuration, this design reduces the peak internal energy by more than 85% and limits the maximum displacement to less than one-third of the baseline value. The results also indicate that a larger charge mass and higher connection stiffness increase the structural energy and deformation, while a larger rise-to-span ratio generally reduces the internal explosion response. The study highlights the importance of combining explosion venting design with geometric optimization to improve the blast resistance of cylindrical lattice shell structures. The findings provide useful guidance for the protective design of large-span structures exposed to internal explosion hazards.

Page 2 of 3 | Total Record : 24

 1   2   3 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 12 No. 4 (2026): April Vol. 12 No. 3 (2026): March Vol. 12 No. 2 (2026): February Vol. 12 No. 1 (2026): January Vol. 11 No. 12 (2025): December Vol. 11 No. 11 (2025): November Vol. 11 No. 10 (2025): October Vol. 11 No. 9 (2025): September Vol. 11 No. 8 (2025): August Vol. 11 No. 7 (2025): July Vol. 11 No. 6 (2025): June Vol. 11 No. 5 (2025): May Vol 11, No 3 (2025): March Vol. 11 No. 3 (2025): March Vol 11, No 2 (2025): February Vol. 11 No. 2 (2025): February Vol 11, No 1 (2025): January Vol 10, No 12 (2024): December Vol 10, No 11 (2024): November Vol. 10 No. 11 (2024): November Vol 10, No 10 (2024): October Vol 10, No 9 (2024): September Vol 10, No 8 (2024): August Vol 10, No 7 (2024): July Vol. 10 No. 7 (2024): July Vol 10, No 6 (2024): June Vol 10, No 5 (2024): May Vol. 10 No. 5 (2024): May Vol 10, No 4 (2024): April Vol 10, No 3 (2024): March Vol 10, No 2 (2024): February Vol 10, No 1 (2024): January Vol 10 (2024): Special Issue "Sustainable Infrastructure and Structural Engineering: Innovations in Vol 9, No 12 (2023): December Vol 9, No 11 (2023): November Vol 9, No 10 (2023): October Vol 9, No 9 (2023): September Vol 9, No 8 (2023): August Vol 9, No 7 (2023): July Vol 9, No 6 (2023): June Vol 9, No 5 (2023): May Vol 9, No 4 (2023): April Vol 9, No 3 (2023): March Vol 9, No 2 (2023): February Vol 9, No 1 (2023): January Vol 9 (2023): Special Issue "Innovative Strategies in Civil Engineering Grand Challenges" Vol 8, No 12 (2022): December Vol 8, No 11 (2022): November Vol 8, No 10 (2022): October Vol 8, No 9 (2022): September Vol 8, No 8 (2022): August Vol 8, No 7 (2022): July Vol 8, No 6 (2022): June Vol 8, No 5 (2022): May Vol 8, No 4 (2022): April Vol 8, No 3 (2022): March Vol 8, No 2 (2022): February Vol 8, No 1 (2022): January Vol 7, No 12 (2021): December Vol 7, No 11 (2021): November Vol 7, No 10 (2021): October Vol 7, No 9 (2021): September Vol 7, No 8 (2021): August Vol 7, No 7 (2021): July Vol 7, No 6 (2021): June Vol 7, No 5 (2021): May Vol 7, No 4 (2021): April Vol 7, No 3 (2021): March Vol 7, No 2 (2021): February Vol 7, No 1 (2021): January Vol 7 (2021): Special Issue "Innovative Strategies in Civil Engineering Grand Challenges" Vol 6, No 12 (2020): December Vol 6, No 11 (2020): November Vol 6, No 10 (2020): October Vol 6, No 9 (2020): September Vol 6, No 8 (2020): August Vol 6, No 7 (2020): July Vol 6, No 6 (2020): June Vol 6, No 5 (2020): May Vol 6, No 4 (2020): April Vol 6, No 3 (2020): March Vol 6, No 2 (2020): February Vol 6, No 1 (2020): January Vol 6 (2020): Special Issue "Emerging Materials in Civil Engineering" Vol 5, No 12 (2019): December Vol 5, No 11 (2019): November Vol 5, No 10 (2019): October Vol 5, No 9 (2019): September Vol 5, No 8 (2019): August Vol 5, No 7 (2019): July Vol 5, No 6 (2019): June Vol 5, No 6 (2019): June Vol 5, No 5 (2019): May Vol 5, No 4 (2019): April Vol 5, No 4 (2019): April Vol 5, No 3 (2019): March Vol 5, No 3 (2019): March Vol 5, No 2 (2019): February Vol 5, No 2 (2019): February Vol 5, No 1 (2019): January Vol 5, No 1 (2019): January Vol 4, No 12 (2018): December Vol 4, No 12 (2018): December Vol 4, No 11 (2018): November Vol 4, No 11 (2018): November Vol 4, No 10 (2018): October Vol 4, No 10 (2018): October Vol 4, No 9 (2018): September Vol 4, No 9 (2018): September Vol 4, No 8 (2018): August Vol 4, No 8 (2018): August Vol 4, No 7 (2018): July Vol 4, No 7 (2018): July Vol 4, No 6 (2018): June Vol 4, No 6 (2018): June Vol 4, No 5 (2018): May Vol 4, No 5 (2018): May Vol 4, No 4 (2018): April Vol 4, No 4 (2018): April Vol 4, No 3 (2018): March Vol 4, No 3 (2018): March Vol 4, No 2 (2018): February Vol 4, No 2 (2018): February Vol 4, No 1 (2018): January Vol 4, No 1 (2018): January Vol 3, No 12 (2017): December Vol 3, No 12 (2017): December Vol 3, No 11 (2017): November Vol 3, No 11 (2017): November Vol 3, No 10 (2017): October Vol 3, No 10 (2017): October Vol 3, No 9 (2017): September Vol 3, No 9 (2017): September Vol 3, No 8 (2017): August Vol 3, No 7 (2017): July Vol 3, No 7 (2017): July Vol 3, No 6 (2017): June Vol 3, No 5 (2017): May Vol 3, No 5 (2017): May Vol 3, No 4 (2017): April Vol 3, No 3 (2017): March Vol 3, No 2 (2017): February Vol 3, No 2 (2017): February Vol 3, No 1 (2017): January Vol 2, No 12 (2016): December Vol 2, No 12 (2016): December Vol 2, No 11 (2016): November Vol 2, No 11 (2016): November Vol 2, No 10 (2016): October Vol 2, No 9 (2016): September Vol 2, No 9 (2016): September Vol 2, No 8 (2016): August Vol 2, No 8 (2016): August Vol 2, No 7 (2016): July Vol 2, No 7 (2016): July Vol 2, No 6 (2016): June Vol 2, No 6 (2016): June Vol 2, No 5 (2016): May Vol 2, No 4 (2016): April Vol 2, No 3 (2016): March Vol 2, No 3 (2016): March Vol 2, No 2 (2016): February Vol 2, No 1 (2016): January Vol 1, No 2 (2015): December Vol 1, No 1 (2015): November More Issue