https://jtie.semnan.ac.ir/ Journal of Transportation Infrastructure Engineering en daily 1 Thu, 23 Oct 2025 00:00:00 +0330 Thu, 23 Oct 2025 00:00:00 +0330 https://jtie.semnan.ac.ir/article_9864.html Incorporating recycled asphalt pavement (RAP) into asphalt mixtures is a common practice for sustainability. However, aging and hardening of the RAP binder can compromise performance. Aged RAP is stiffer, less flexible, and more prone to cracking. This research investigates the combined effects of RAP content, RAP age, and nano-silica (SiO₂) addition on asphalt performance, focusing on fracture resistance, fatigue resistance, and moisture susceptibility. Asphalt mixtures containing 25%, 50%, and 75% RAP were evaluated using RAP aged 5 and 10 years. Nano-SiO₂was incorporated at 0%, 1%, 1.5%, and 2%. Performance evaluations included semi-circular bending test for fracture resistance, indirect tensile fatigue test for fatigue resistance, and tensile strength ratio (TSR) test for moisture susceptibility. The findings indicated that nano-SiO₂significantly enhanced fracture and fatigue resistance, especially at lower RAP content. At 25% RAP, 1.5% and 2% nano-SiO₂improved both fracture and fatigue resistance by 15%-20%. Notable enhancements were observed for mixtures with 50% and 75% RAP, with optimal results at 2% nano-silica. Furthermore, nano-SiO₂improved moisture resistance, increasing TSR values above 80%. Nano-silica effectively mitigated aging effects, particularly in mixtures with higher RAP content. Based on this analysis, utilizing 25%-50% RAP combined with 1.5% nano-SiO₂is advisable for producing high-performance asphalt mixtures. https://jtie.semnan.ac.ir/article_10152.html Geopolymer concrete (GPC) is gaining attention as an environmentally friendly alternative to traditional cementitious concretes. However, its performance in high-temperature environments and in operational conditions such as pavements requires improvement. This research was conducted with the aim of investigating the enhancement of thermal performance and durability of GPC when exposed to elevated temperatures. In this experimental study, fly ash and metakaolin were utilized as precursor materials in a binary blended GPC, and the effect of adding Polypropylene (PP) fibers at various volume fractions (up to 1.5%) on the mechanical properties and thermal performance of the concrete was examined under different temperature conditions (room temperature, 200, 500, and 800 °C). Compressive strength of the specimens was measured at 7 and 28 days, and Analysis of Variance (ANOVA) was employed to determine the degree of influence of each factor on compressive strength. The results indicated that addition of PP fibers up to 0.5% increased the GPC’s compressive strength, and optimum replacement percentage for fly ash with metakaolin was 20%, where compressive strength of the specimens showed a significant improvement. This research demonstrated that incorporation of polypropylene fibers and using metakaolin can enhance the thermal performance (fire resistance) of binary blended GPC and ensure its durability in pavement applications at high temperatures. These findings offer practical solutions for improving the high-temperature performance of GPC, paving the way for its wider applications. https://jtie.semnan.ac.ir/article_10170.html In excavation projects, presence of convex corners makes 2D analyses inaccurate. This study investigates the effect of these corners on deformation of retaining walls using 3D modeling. The present study uses FLAC3D software for two-dimensional and three-dimensional numerical modeling to study the effect of convex corners on the deformations caused by excavation for walls stabilized by anchor rod method. It also examines the effect of various parameters such as soil type, soil adhesion, pit height and reliability coefficient on the deformation rate of convex corner. Results of the present study show that the length of corner-affected area (area around the convex corner where values of the three-dimensional displacement ratio at the location of crown pit is greater than the two-dimensional corresponding value) decreases with the decrease in soil resistance. So, in weak, medium and strong soil, it is 2H, 1.5H and 1.25H respectively. Due to the influence of three-dimensional geometry of convex corner, the three-dimensional reliability coefficient values are lower than corresponding two-dimensional values. https://jtie.semnan.ac.ir/article_10158.html Geosynthetic-reinforced soil walls, due to advantages such as ease of construction, cost-effectiveness, and favorable performance, are widely used in civil engineering projects. However, the presence of uncertainties in the strength parameters of the soil and reinforcing materials poses a significant challenge in the analysis and design of these structures. The present study, aimed at improving the accuracy of safety and performance assessment of reinforced soil walls, employs a probabilistic approach using the Limit Equilibrium Method (LEM) combined with Monte Carlo Simulation (MCS). Input parameters (such as dry unit weight, friction angle, and cohesion) were considered as random variables, and their influence on the factor of safety was evaluated. Results indicate that increasing the soil internal friction angle from 19° to 29° raised the factor of safety from 0.8 to 1.1, while reducing the probability of failure from about 0.05 to 0.0005. A geosynthetic length-to-wall height ratio (L/H) greater than 0.8 and geosynthetic layer spacing of less than 0.6 m also improved stability (probability of failure decreased from 0.0007 to 0.001). In contrast, increasing the soil unit weight of abutment up to 22 kN/m³ resulted in a reduction of the factor of safety. Comparison of the study results with code-based criteria (FS = 1.5) revealed that, in cases such as an internal friction angle of 29° and L/H = 1, the obtained factor of safety was about 15% higher than the reference code value, whereas in conditions such as increasing the backfill unit weight to 22 kN/m³, it was about 20% lower than the code requirement. Therefore, adopting probabilistic analysis can provide a more realistic and accurate estimation of uncertainty compared to deterministic code-based design https://jtie.semnan.ac.ir/article_9900.html Selecting an appropriate superstructure construction method is crucial for the success of bridge projects. This study aims to evaluate and prioritize various bridge construction methods using a multi-criteria decision-making approach based on the Analytic Hierarchy Process (AHP). Five key criteria—quality, cost, safety, project duration, and structural form—were identified as the most influential factors in the decision-making process. The statistical population included engineers, contractors, consultants, and supervisors involved in bridge construction projects, from which 68 experts were selected through convenience sampling. Data collection was conducted using both library research and field methods, including interviews and questionnaires. Data analysis was carried out using Expert Choice software. The results indicate that the cable-stayed bridge construction method ranked highest with a priority weight of 0.280, making it the most suitable option for bridge implementation in Iran. Following this, incremental launching, prestressed concrete, precast concrete, span-by-span steel, balanced cantilever steel, and cast-in-place concrete methods were ranked accordingly. Among the criteria, safety was identified as the most significant factor across most construction methods, except for the prestressed concrete method, where quality held the highest importance. The findings of this study offer valuable insights for decision-makers in selecting bridge construction methods under various project conditions. https://jtie.semnan.ac.ir/article_10144.html In the past, engineers were not familiar with pavement management and only paid attention to pavement maintenance. Pavements that require repair and maintenance should be selected for repair and maintenance before reaching a critical state, and this operation requires the experience of engineers based on the pavement management status. Prioritizing road maintenance is one of the most essential measures for allocating funds to road and pavement improvement projects. The simultaneous use of different factors in conventional project prioritization models seems to be essential. In this study, using factors such as Damage Index (PCI), traffic and service level, surrounding uses, accident rates, and machinery, the maintenance of several important streets in Hamedan city has been prioritized using the Analytical Network Process (ANP) method. Results of the sensitivity analysis showed that Axis 2 (Mahdieh Street) with a weight of 0.39 and Axis 1 (Sadaf Street) with a weight of 0.27 have the lowest priority. This indicates that relying on a single criterion cannot provide a correct analysis of the condition of the existing roads. https://jtie.semnan.ac.ir/article_10314.html Dynamic modulus (|E*|) and phase angle (φ) are key parameters for describing the viscoelastic performance of asphalt mixtures. However, their experimental evaluation involves lengthy testing and costly laboratory procedures. To overcome these limitations, several predictive models have been introduced, among which the Witczak and Hirsch models are the most recognized. In recent years, machine learning (ML) techniques have gained attention in engineering applications due to their strong capabilities in data analysis, optimization, and prediction. This study introduces the K-Nearest Neighbors (KNN) algorithm as an ML-based method to estimate the viscoelastic behavior of asphalt mixtures. The model was trained and validated using an extensive dataset comprising bitumen characteristics, volumetric parameters, and measured values of dynamic modulus and phase angle at various temperatures and loading frequencies, totaling over 5500 data points. The results demonstrate that the proposed ML model provides high prediction accuracy and represents a promising alternative for estimating the viscoelastic properties of asphalt mixtures. https://jtie.semnan.ac.ir/article_10005.html Hot Mix Asphalt (HMA), although widely used in pavement construction due to its favorable mechanical properties, is highly affected by environmental and chemical conditions of surface runoff. The aim of this study is to investigate the effect of runoff pH variations (4, 7, and 9) on the adhesion between bitumen and aggregates and the mechanical failure parameters of asphalt mixtures. To this end, a series of laboratory tests, including Indirect Tensile Strength and moisture sensitivity (ITS/TSR), Semi-Circular Bending (SCB) at low temperature, Indirect Tensile Fatigue Test (ITFT), and contact angle measurements for Surface Free Energy (SFE) analysis were performed. The results indicated that acidification of the environment (pH = 4) significantly increased the contact angle and reduced the total surface free energy of bitumen from about 46.14 to 52.12 (ergs/cm²), reflecting a decline in wettability and adhesion. TSR values decreased markedly under acidic conditions, while a relative improvement was observed in alkaline conditions. In addition, the Indirect Tensile Strength and SCB indices (peak load and fracture energy) were considerably reduced in the acidic environment; for example, the maximum load of the samples dropped by more than 20% compared to the neutral condition. Fatigue tests showed that fatigue life decreased sharply in acidic conditions, whereas alkaline conditions provided a relative improvement compared to neutral conditions, although still lower than that of the dry state. Overall, the findings demonstrate that acidic runoff has the most detrimental effect on the adhesion and mechanical durability of HMA, while alkaline conditions may partially compensate for moisture-induced damage. These results highlight the importance of considering environmental chemical conditions in the design and maintenance of pavements, particularly in urban and industrial areas with a high probability of acidic runoff https://jtie.semnan.ac.ir/article_10342.html The use of Nano-Al₂O₃ to enhance the durability of asphalt mixtures containing expanded clay aggregates (LECA) represents a sustainable and innovative approach in pavement engineering. In this study, 85–100 bitumen was blended with 0, 1%, 2%, and 3% by weight of Nano-Al₂O₃ using a high-shear mixer, and the bitumen was subjected to physical and rheological tests, including penetration, softening point, ductility, rotational viscosity, RTFO, PV, and DSR. Asphalt mixtures with 0%, 25%, and 50% replacement of LECA aggregates were evaluated through Marshall, dynamic creep, and fatigue life tests to assess the combined effect of these additives on mechanical properties, creep behavior, and fatigue resistance. The physical tests of bitumen indicated that Nano-Al₂O₃ addition improved the softening point, provided controlled stiffness, enhanced ductility, and increased viscosity. Rheological results showed that Nano-Al₂O₃ reduced mass loss and improved rutting and fatigue indices, indicating delayed cracking and increased pavement durability. Dynamic creep tests revealed that the incorporation of Nano-Al₂O₃ and optimal LECA replacement reduced cumulative strain and slowed the accelerated growth rate in the tertiary creep phase. Fatigue life tests demonstrated that the 25% LECA + 2%Al₂O₃ mixture exhibited the best performance, confirming that although elevated temperature and high stress levels accelerate fatigue damage, this mixture effectively mitigated these adverse effects and delayed deterioration. Regression modeling indicated that the percentages of Nano-Al₂O₃ and LECA aggregates had a significant impact (p < 0.05) on fatigue life. The models achieved high coefficients of determination (R² = 0.928, 0.947, and 0.937 for 0%, 25%, and 50% LECA, respectively), accurately predicting the fatigue behavior of the mixtures. These results demonstrate the suitability and generalizability of the models for the design of durable asphalt pavements. https://jtie.semnan.ac.ir/article_10343.html ABSTRACT In this study, the main objective is investing the effect of adding nanoclay on the stability of excavations. For this purpose, laboratory soil samples were prepared in two conditions: untreated and treated with different percentages of nanoclay (0%, 3%, and 5%), and subsequently tested. Then, a 10-meter-deep excavation model in the Velenjak area of Tehran was constructed at a smaller scale under both natural and nanoclay-stabilized conditions, and the deformations were examined. In addition, numerical modeling of the target excavation was carried out using FLAC3D software to provide a comparison between the laboratory and numerical results. The findings indicated that the use of nanoclay significantly increases the factor of safety of excavation stability and reduces both horizontal and vertical displacements. The optimum nanoclay content was found to be 3%, as higher percentages showed no significant improvement and, in some cases, even had negative effects on soil behavior. This behavior was observed in both numerical and experimental models, with good agreement between them. Also, the influence of nanoclay on soil strength as a novel material was compared with cement as a conventional stabilizer. The advantages of nanoclay—particularly its environmentally friendly nature compared to cement—were highlighted as strong reasons to prefer nanoclay and, more generally, nanomaterials. Overall, the results demonstrated that applying nanoclay as a stabilizing additive can serve as an effective solution for urban excavation projects. https://jtie.semnan.ac.ir/article_10353.html The composite foundation reaction modulus (K∞) is a vital parameter in concrete pavement design, characterizing pavement-subgrade interaction behavior and directly influencing stress distribution and deformation in concrete slabs. Traditional determination methods relying on code-based design charts (Code 731 and AASHTO 1993) face data extraction challenges that limit modeling accuracy. This study develops an innovative numerical model for K∞ using data extracted from these charts, assuming a semi-infinite subgrade. Primary objectives include precise chart data extraction, fitting regression models (linear, polynomial, exponential, power, logarithmic), and selecting the optimal mathematical relationship using rigorous statistical criteria. Model performance was evaluated via coefficient of determination (R²), adjusted R², standard error of estimate (SEE), and residual analysis. Results indicate the power model achieves superior statistical performance with R² = 97.41% and low SEE = 0.1308, alongside optimal residual behavior, explaining K∞’s nonlinear dependence on subbase thickness, subbase elastic modulus, and subgrade soil modulus. Analysis revealed K∞ is critically influenced by: synergy between subbase thickness and elastic modulus; subbase’s protective effect on subgrade, and; a subbase thickness threshold condition. These mechanical interactions govern pavement-subgrade system stiffness. The research provides an efficient methodology to convert design charts into analytical equations, enhancing accuracy, reducing human error, and enabling integration into sensitivity analyses and concrete slab thickness optimization under variable subbases. https://jtie.semnan.ac.ir/article_10374.html Linear and alligator cracking are critical indicators of asphalt pavement performance. The accurate prediction of these ciritical cracking distresses are of significant importance in effective and efficient pavement maintenance planning. This study proposes a data-driven framework based on multimodal data from the Long-Term Pavement Performance (LTPP) database, incorporating traffic, climatic, and performance-related variables to predict distress severity, length, and area. Key features, including surface distress indices, overlay thickness, traffic characteristics, and climatic indicators, were extracted and refined through feature engineering. Machine learning-based models were developed for severity classification and quantitative distress prediction using Artificial Neural Networks (ANNs). Addressing class imbalance with SMOTE improved severity classification accuracy from 0.782 to 0.843 for linear cracking and from 0.845 to 0.930 for alligator cracking. The models demonstrated strong predictive performance, achieving R² values of 0.941 for linear crack length and 0.954 for alligator crack area, supporting their applicability in preventive maintenance and pavement life-cycle management. https://jtie.semnan.ac.ir/article_10495.html Pavement networks play a vital role in national transportation infrastructure and economic growth by enabling the safe, rapid, and economical movement of goods, services, and people. The quality of these networks is heavily influenced by various types of distress; therefore, accurate prediction of such distress is essential for effective pavement management. Furthermore, machine learning models have recently demonstrated significant potential in modeling pavement performance. This study aims to employ machine learning models to predict the functional condition of pavements, specifically the International Roughness Index (IRI). The research data were extracted from the Long-Term Pavement Performance (LTPP) database managed by the U.S. Federal Highway Administration. The dataset comprises 4,453 records related to pavement structure and construction, weather, traffic, and pavement performance, encompassing 12 effective variables. Seven machine learning algorithms—Decision Tree, Random Forest, XGBoost, Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, and Artificial Neural Network—were used to predict the IRI. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Comparative analysis revealed that the XGBoost and Random Forest algorithms outperformed the others in predicting IRI, with MAE values of 0.17 and 0.18 and R² values of 0.73 and 0.74, respectively. The model developed in this research can serve as a precise and practical tool in pavement management systems for timely roughness prediction and optimizing maintenance programs and budget allocation. https://jtie.semnan.ac.ir/article_10536.html Considering the various uses of different types of asphalt mixtures in the world, the evaluation of the different behaviors of this mixture is significant in terms of performance and safety. Despite extensive research on the self-healing properties of asphalt mixtures, the specific impact of carbon nanotube (CNT)-modified Gilsonite asphalt binder on self-healing performance has not been thoroughly investigated. This study fills this gap by exploring the synergistic effects of CNT and Gilsonite on the self-healing behavior of asphalt mixtures, providing new insights into enhancing pavement properties. The goal of this study was to examine the self-healing behavior of asphalt mixtures containing asphalt binders modified with CNT. For this purpose, 7 wt% of Gilsonite-modified asphalt binder, as well as 0.05, 0.1, 0.2, and 0.3 wt% of CNT-modified asphalt binder, were added to the virgin binder with penetration grade 60/70 and mixed by a high-shear mixer. Then, the four-point bending test was conducted on the modified asphalt binder samples and examined under the constant strain test conditions at 500 and 800 microstrain levels. The results indicated that the addition of CNT to Gilsonite-modified asphalt binder had a remarkable impact on increasing the self-healing characteristics of asphalt samples. Moreover, results showed that the healed samples recovered the stiffness from a minimum of 69% to a maximum of 99% compared to the base samples. Also, the self-healing rate was considerably different between the base asphalt sample and mixtures modified with Gilsonite and CNT under constant strains. The findings suggest that incorporating CNT into Gilsonite-modified asphalt binder significantly enhances self-healing properties, with potential applications in extending the lifespan of pavements and reducing maintenance costs, and as a result, the economic efficiency and sustainability of transportation infrastructure increase. https://jtie.semnan.ac.ir/article_10546.html ABSTRACT Increasing environmental threats from cement manufacturing and disposal of plastic and polymer waste necessitated the implementation of sustainable alternatives in soil stabilization. This study focuses on the effects of application of cement and recycled high-density polyethylene (HDPE) granules on the strength, and microstructure of sandy soil. The mixtures were prepared with different amounts of cement (5%, 8%, and 11% of dry weight) and recycled HDPE granules (2%, 4%, 6%, and 8% of dry weight) and were subjected to UCS and ITS testing. The microstructural study was also done using scanning electron microscopy (SEM). Tests showed that increasing cement content from 5 to 11% produced compressive strength increases of 340% and a 261% increase in tensile strength over the control sample, which made it more brittle in terms of mechanical response; however, incorporating recycled HDPE granules at the optimal amounts (4-6%) made the mixture more ductile and better overall mechanically. Moreover, the results indicated that incorporating HDPE granules within the range of 4–6%, in conjunction with the optimum cement content, can effectively enhance the durability and strength retention of the stabilized soil under wetting–drying cycles, whereas higher contents lead to a reduction in performance. SEM observations showed that HDPE granules were compatible with the cementitious matrix at these optimum levels. The findings suggest that recycled HDPE granules are able to be used in optimal amounts to improve the mechanical performance of cement-stabilized soils, whereas the sustainable management of polymer waste in geotechnical engineering is served better. Unlike earlier studies that mostly employed virgin or non-recycled polyethylene on clayey soils, This study for the first time employs recycled HDPE granules to enhance the mechanical behavior of sandy soils and thus brings novel insights into the mechanisms by which their strength is improved.