https://jstt.vn/index.php/en/issue/feedJournal of Science and Transport Technology2025-06-30T00:00:00+00:00Binh, Pham Thaibinhpt@utt.edu.vnOpen Journal Systems<p><img class="img-responsive" src="https://jstt.vn/public/journals/1/jstt_scopus.png" alt="JSTT has been accepted in Scopus" /></p> <p>Journal of Science and Transport Technology (JSTT) (E-ISSN: <a href="https://portal.issn.org/resource/ISSN/2734-9950">2734-9950</a>) under the publisher of <a href="https://utt.edu.vn/">University of Transport Technology (UTT)</a> has been granted permission by the Ministry of Information and Communication, Vietnam, under Document No. 399/GP-BTTTT dated June 29, 2021, to publish issues in English. JSTT is indexed in <a href="https://www.scopus.com/sourceid/21101274771?origin=resultslist">SCOPUS</a> and <a href="https://scholar.google.com/citations?hl=vi&user=7PS1tesAAAAJ&view_op=list_works&sortby=pubdate">Google Scholar</a>. All published papers are assigned a <a href="https://www.doi.org/">DOI</a> and are registered with <a href="https://www.crossref.org/">Crossref</a>. To ensure academic integrity, each submission is thoroughly checked for similarity using the <a href="https://www.ithenticate.com/">iThenticate</a> tool to prevent plagiarism.</p> <p>JSTT is dedicated to continuously enhancing the quality of its published articles and online editorial system to meet international standards. It serves as a prestigious platform for local and international scientists to exchange and publish new research findings, supporting scientific advancements and industry applications. In its pursuit to solidify its international standing, the Journal is actively seeking contributions from domestic and international scientists.</p> <p>JSTT is a peer-reviewed scientific journal specializing in the field of construction, covering the following areas: building and industrial construction; bridge and road engineering; coastal, offshore, and hydraulic engineering; materials science; mechanical engineering; architecture and urban planning; economics and management; environmental engineering; natural sciences; and information technology. Through continuous development in both quantity and quality, the Journal has steadily established itself as a premier scientific and technological publication in the field of civil engineering construction; applied and natural sciences.</p> <p align="justify">JSTT publishes high-quality original research articles, review articles, and technical notes covering various aspects of science and technology, particularly focusing on infrastructure development. It encompasses the following areas, with a scope that extends beyond these:</p> <p align="justify">- Transport planning and traffic engineering<br />- Civil and structure engineering<br />- Construction materials<br />- Mechanical engineering<br />- Geotechnical engineering<br />- Earth and Environmental Engineering<br />- Computer sciences<br />- Electricity, electronics, telecommunications<br />- Automotive engineering</p> <ul> <li><a href="https://jstt.vn/index.php/en/about#aim-and-scope"><strong>Aim and scope</strong></a></li> <li><a href="https://jstt.vn/index.php/en/about#peer_review_process"><strong>Peer Review Process</strong></a></li> <li><strong><a href="https://jstt.vn/index.php/en/about#public_frequency">Publication Frequency</a><br /></strong></li> <li><a href="https://jstt.vn/index.php/en/about#article_processing_charge"><strong>Article Processing Charge (FREE)</strong></a></li> <li><a href="https://jstt.vn/index.php/en/about#licence"><strong>License</strong></a></li> <li><a href="https://jstt.vn/index.php/en/publication_ethics"><strong>Publication Ethics and Malpractice Statement</strong></a></li> <li><a href="https://jstt.vn/index.php/en/guide-for-authors"><strong>Guide for authors</strong></a></li> <li><a href="https://jstt.vn/index.php/en/about#journal-policies"><strong>About the Journal</strong></a></li> </ul>https://jstt.vn/index.php/en/article/view/35Influence of Graphene Oxide (GO) and Fly Ash (FA) on the workability and mechanical properties of self-compacting concrete2025-03-24T03:00:58+00:00Nguyen Thi Thu Ngangantt@utt.edu.vnLe Van Kienkienlv@utt.edu.vnDoan Lan Phuongphuongdl@utt.edu.vnNguyen Thi Huehuent@utt.edu.vnMinh Tran Quangminhtq@civil.uminho.pt<p>Self-compacting concrete (SCC) is an advanced material for complex construction applications requiring accurate formwork, high reinforcement density, and superior surface finishes. The ability to flow and consolidate under its weight eliminates the need for mechanical vibration, thereby improving construction efficiency, reducing labor costs, and ensuring greater homogeneity. SCC is widely employed in underground structures, high-rise buildings, and load-bearing elements where strength, durability, and workability are critical. Graphene oxide (GO) and fly ash (FA) enhance SCC's performance. GO enhances compressive strength, crack resistance, and durability through microstructural refinement and crack propagation inhibition, while FA improves workability, reduces water demand, and increases durability by lowering permeability and shrinkage. The partnership between GO and FA meets SCC performance standards and enhances its applicability in high-performance and sustainable construction. This study examined the effects of GO content (0% and 0.03% by binder weight) and FA content (15%, 25%, and 35%) on the workability and mechanical qualities of SCC. A series of studies were performed to assess the workability features of SCC, together with two principal mechanical properties: compressive strength and flexural tensile strength at 28 days. The findings demonstrate that while an increase in GO content diminishes flowability owing to its elevated specific surface area, the appropriate incorporation of FA mitigates this effect, leading to a combination with enhanced mechanical properties. SCC with 0.03% GO and 25% FA attained the maximum compressive strength at 28 days.</p>2025-05-14T00:00:00+00:00Copyright (c) 2025 Journal of Science and Transport Technologyhttps://jstt.vn/index.php/en/article/view/324An application of the response surface method to structural optimization problems2025-04-12T13:32:04+00:00Anh Tuan Trantuanta@utt.edu.vnNhu Son Doanvanson.ctt@vimaru.edu.vn<p>Structural problems are commonly analyzed using implicit solvers, such as the finite element method (FEM), which often demand manual processes and are computationally intensive, especially for structural optimization problems that involve multiple FEM evaluations to identify the optimal design solutions. In addition, implementing optimization algorithms also demands significant expertise for accurate application. This study proposes a simple yet efficient procedure to address structural optimization problems by transforming the implicit analysis into explicit performance functions using the response surface method and simulating the space of input variables by random samples. The explicit performance functions enable quick evaluations for all generated samples of inputs, and a search routine is developed to identify optimal solutions efficiently. The proposed procedure is validated through three case studies, demonstrating its ability to achieve accurate solutions within minutes of analysis.</p>2025-05-14T00:00:00+00:00Copyright (c) 2025 Journal of Science and Transport Technologyhttps://jstt.vn/index.php/en/article/view/354Enhancing Seismic Performance of Reinforced Concrete Exterior Joints with Ultra-high-performance Steel Fiber Reinforced Concrete: A Parametric Finite Element Study2025-03-25T15:20:43+00:00Trung-Hieu Tranhieutt@hau.edu.vn<p>Beam-column joints are vital to the stability and performance of reinforced concrete (RC) frame structures, particularly under seismic conditions. Understanding their stress-strain behavior is crucial for evaluating their capacity and ductility. However, performing detailed experimental studies with numerous specimens is often impractical due to significant costs and time constraints. As a result, finite element (FE) analysis, supported by tools like ABAQUS, has become a preferred approach for studying joint behavior effectively. This study employs the finite element method (FEM) to analyze exterior beam-column joints designed for high ductility (DCH) and enhanced with ultra-high-performance steel fiber reinforced concrete (UHPSFRC). The FE results are validated against experimental data through comparisons of load-displacement responses, failure patterns, and reinforcement strain progression. Furthermore, the research examines the effects of parameters like UHPSFRC strengthening length, axial column load, and steel fiber content on joint tensile stress, providing insights into optimizing seismic performance.</p>2025-05-14T00:00:00+00:00Copyright (c) 2025 Journal of Science and Transport Technologyhttps://jstt.vn/index.php/en/article/view/356Mapping Cadmium Contamination Potential in Surface Soil for Civil Engineering Applications: A Comparative Study of Machine Learning and Deep Learning Models in the Gianh River Basin, Vietnam2025-04-19T15:41:06+00:00Vuong Hong Nhatnhatmit@gmail.comPhan Trong Trinhphantrongt@yahoo.comLai Vinh Camlvcamminh04@yahoo.comBui Tien Dieubuitiendieu@gmail.comLe Van Hiephieplv@utt.edu.vnIndra Prakashindra52prakash@gmail.comNguyen Ngoc Anhanhnn@imer.vast.vnNguyen Van Hongnguyenhong.ig@gmail.comNguyen Duc Thanhducthanh.viendiali@gmail.comNguyen Phuong Thaophuongthaoblue@gmail.comNguyen Thi Thu Hienthuhienkien@yahoo.comTran Thi Nhungttnhungvfu@gmail.comTran Trung Hieutrunghieu95ctb@gmail.comTran Van Phongtphong1617@gmail.com<p>Cadmium (Cd) is a toxic heavy metal with significant environmental and human health risks, particularly when accumulated in surface soils. Its presence reduces soil fertility, disrupts microbial ecosystems, and poses long-term ecological threats. This study explores the application of artificial intelligence (AI) models for mapping the potential distribution of Cd contamination in surface soils within the Gianh River Basin, Quang Binh Province, Vietnam. Four machine learning (ML) models Logistic Regression (LR), Radial Basis Function Network (RBFN), Random Forest (RF), and Support Vector Machine (SVM) and four deep learning (DL) model variants (DNN-Opt1 to DNN-Opt4) were developed and compared. The DNN variants differ based on the configuration of hidden layers and neuron counts.</p> <p>A total of 100 topsoil samples were collected and classified using the Geoaccumulation Index (Igeo), serving as the target variable for supervised learning. Thirteen conditioning factors were used as input variables, including Elevation, Soil Type, Slope, Curvature, proximity to roads and rivers, and seven Landsat 8 spectral bands. The dataset was divided into training (70%) and testing (30%) subsets. Model performance was evaluated using multiple metrics, including the area under the ROC curve (AUC), accuracy (ACC), Kappa coefficient, root mean square error (RMSE), and confusion matrix.</p> <p>Among the tested models, the DNN-Opt2 variant demonstrated the highest predictive performance with AUC = 0.858, ACC = 73.33%, Kappa = 0.47, and RMSE = 0.45. The resulting contamination potential map, particularly that derived from the RBFN model, categorized the region into five contamination risk levels: very low, low, moderate, high, and very high. This spatial information is critical not only for environmental management but also for assessing risks to groundwater quality and the structural integrity of buildings located in high-risk zones. The study demonstrates the efficacy of deep learning in enhancing predictive accuracy for heavy metal contamination mapping and underscores its practical relevance in civil and environmental engineering applications.</p>2025-05-22T00:00:00+00:00Copyright (c) 2025 Journal of Science and Transport Technologyhttps://jstt.vn/index.php/en/article/view/372Pre-stressed Ultra High Performance Concrete Slab Subjected to Blast Impact - A numerical study2025-05-11T12:12:40+00:00Ba Danh Ledanhlb@huce.edu.vnTrong Lam Nguyenmaivietchinh@lqdtu.edu.vnViet Chinh Maimaivietchinh@lqdtu.edu.vnChi Hieu Daomaivietchinh@lqdtu.edu.vnTien Dat Tranmaivietchinh@lqdtu.edu.vn<p>The purpose of this article is to study the blast resistance capacity of Pre-stressed Ultra High Performance Concrete slab. Three-dimensional numerical models, including the explosion source, Ultra High Performance Concrete (UHPC) slab reinforced by pre-stressed tendons and steel bars under boundary conditions, are developed to simulate the full-scale behavior of the structure under blast load. The proposed simulation model is validated through comparison with the results of a prior experimental study. Chosen explosions with the stand-off distance and TNT charge weight in two cases of close-in and near-field regime. The analysis results are compared to the UHPC slab reinforced by steel bars and High Strength Concrete (HSC), with a focus on blast resistance capacity and the level of damage reduction in the structure. The results of the analysis indicate that HSC slabs sustain a 70% higher level of severe damage compared to UHPC slabs reinforced with pre-stressed tendons. The dynamic increase factor of UHPC is determined to be 32% larger than HSC. Concerning UHPC slabs, while the pre-stressed tendon serves as crucial in decreasing deflection of slabs under blast loads, its influence on altering the damage level of the structure is less pronounced.</p>2025-06-04T00:00:00+00:00Copyright (c) 2025 Journal of Science and Transport Technologyhttps://jstt.vn/index.php/en/article/view/376Investigation of Cable Tension in Cable-Stayed Bridges Through Field Measurements and Numerical Simulation2025-05-05T03:49:56+00:00Duc Thi Thu Dinh Nguyennguyenthudinh@utc.edu.vnTuan Ngoc Nguyenntngoc@freyssinet.com.vnAleena SaleemAleenaSaleem@gmail.comJae Hyun ParkJaeHyunPark@gmail.comHoai Hohoaiho@utc.edu.vn<p>This study investigates cable tension forces in a one-plane cable-stayed bridge in Vietnam using field measurements and numerical simulation. Cable forces obtained from the Finite element method (FEM) are compared with design values and field-measured data from lift-off and vibration-based method. Results show that field-measured forces generally deviate within 7% of the design values, confirming their reliability. Both measurement methods effectively capture cable force variations, with low tension in long cables and high tension in shorter ones. Numerical simulations accurately represent cable rigidity, with frequency discrepancies remaining below 3%. However, larger errors of 12% to 15% occur in shorter cables near the tower, while longer cables closely align with design values within 3%. Despite these differences, simulation-based preliminary analysis is acceptable for minimizing field measurements and serves as a valuable reference for structural assessments in service stage.</p>2025-05-28T00:00:00+00:00Copyright (c) 2025 Journal of Science and Transport Technology