Iranian Journal of Science and Technology, Transactions of Civil Engineering最新文献

High-Performance Concrete (HPC) is an exceptional concrete with remarkable performance mostly in all aspects and has a compressive strength more than 60 MPa. This paper investigates the characteristics of concrete by various mechanical tests like compressive, split tensile and flexural strength with the reinforcement of different types of fibres and incorporation of graphene oxide. The microstructural analysis was also done to study the effects of different materials on the concrete. The usage of various types of fibres, Graphene oxide, mineral admixtures, preparation techniques and the utilization of materials in hybrid combinations are being investigated. Denser microstructure, lesser porosity and a homogeneous mixing are the basic requirements of the HPC design. Due to the requirement for a huge quantity of cement in HPC which is responsible for CO₂ emission, abrasion and excessive heat of hydration resulting in cracks, Supplementary cementitious constituents like fly ash and silica fume were used, which also reduces the cost of construction. The nanomaterials react with calcium hydroxide and creates increased C–S–H gels, also aids in attaining a denser microstructure for HPC by filling the voids and pores, thereby providing sites for the nucleation and formation of C–S–H gel. It also helps in reducing the development of nano cracks, while the fibres in concrete helps in the energy dissipation effect during loading conditions and also produces a bridging effect for micro and macro cracks. The compressive, split tensile and flexural strength was observed to be improved up to 30.65%, 91.2% and 89.58% with the reinforcement by the hybrid combination of fibres and nanomaterials. The microstructural analysis on the concrete showed petal like crystals and a denser microstructure, with the usage of graphene oxide. Higher C–S–H and calcium hydroxide crystals formation was also noticed due to the usage of mineral admixtures and graphene oxide. The bridging effect of fibres hold firm in concrete matrix were also seen on the microstructural analysis. Based on the investigations, it has been found that the hybrid usage of the medium hooked end steel fibres, micro basalt fibres and Graphene oxide aides in improving several properties of the HPC.

A manufacturing fault causes a defect consisting of a crack in the structure. Identification and classification are essential in scientific research because cracks can lead to catastrophic system failure. The purpose of structural fitness tracking is to diagnose and predict structural fitness. A complete crack detection method based on free vibration is widely used to find potential cracks in systems. However, static deflection methods are limited to predicting crack parameters. Therefore, this article uses the static deflection method to determine the crack locations and depth in the cantilever beam. A dead weight was attached to the beam’s free end, and two dial gauges were used. A gauge was attached to the free end of the beam to measure the free-end deflection. Another dial indicator was also installed near the crack to measure the static deflection of the crack. Numerical and experimental analyses were performed on 48 cracked specimens to measure the static deflection at two points. A regression model was developed to calculate the crack parameters, i.e., crack locations and crack depths in beams. To evaluate the reliability of the developed regression model, a machine learning model, i.e., Artificial Neural Network (ANN) and Random Forest (RF), was used for prediction. Regression, ANN, and RF models were developed using numerical and experimental datasets. The crack depth and location results obtained from the regression and machine learning models are consistent with the actual results. The crack parameters were predicted using static two-point deflection as input, and the results were encouraging. Therefore, the static two-point deflection approach may be widely used to detect future cracks in more complex structures.

As more lanes are often added to facilitate mobility in road infrastructure, the demand for effective crash barrier safety elements has grown with time. There is also a greater need for thin crash barrier configurations worldwide, which occupy less space. The goal of the current study is to develop a prefabricated textile-reinforced concrete (TRC) crash barrier that is non-corrosive and has a thin wall. A first-of-its-kind pre-fabrication procedure that completely avoids demoulding has been devised for the manufacturing of TRC crash barriers. Investigations were carried out on TRC crash barriers under static loading to determine the influential aspects such as the textile material (glass and basalt), the number of textile layers and their positioning, and the influence of different types of fine aggregates in the TRC binder. Further, factorial analysis is utilized to ascertain the individual and interaction impacts of major elements important to the development of the TRC crash barrier. Conclusions concerning TRC’s appropriateness for both temporary and long-term deployment as crash barriers are made in light of the research.

This paper investigated the dynamic response of plain concrete with three different strength grades, namely C30, C40, and C50, at an early age (3, 7, 14, and 28 days, respectively). Significant patterns were uncovered using a 75-mm-diameter split Hopkinson pressure bar (SHPB) apparatus. Initially, pronounced viscoelastic behaviour was observed in the concrete’s early stages, characterised by a sharp stress-strain curve both before and after reaching its peak. As the concrete matures, stress concentration within the stress-strain curve becomes more pronounced. Additionally, exponential growth in dynamic strength with higher strain rates was observed, while the strain rate index decreased with age. Improving concrete quality was found to reduce the sensitivity of dynamic strength to strain rate. A viscoelastic damage constitutive model was formulated based on experimental analysis to describe the mechanical response effectively. The evolution of concrete properties over time was accurately captured by fitting model parameters to the experimental data. The theoretical stress-strain curves derived from this damage model closely matched experimental curves across various ages.

This paper puts forward in-depth investigations on possible local site effects on damage distribution during the destructive 2003 Bam earthquake of southeastern Iran. In this investigation, detailed geotechnical characteristics of soil layers including subsurface soil profile, shear-wave velocity (V_S) profiles along with constitutive soil models were surveyed in different areas of the city. A series of one-dimensional dynamic site response analyses were carried out to evaluate possible local site effects during the earthquake and also to propose site-specific design spectra for the city of Bam. Based on field and aerial surveys after the earthquake, a damage distribution map was prepared and compared with local site effects obtained from the analyses. The calculated PGA values at ground surface in the eastern part of the city were higher than those in the western zone while the maximum PGA occurred in the central part of the city. It was found that the local site conditions affected the pattern of PGA distribution, primarily due to the existence of a thick sedimentary layer extended from the northwest towards the southeast of the city. The distribution of period of maximum amplification complies with the result of geotechnical and geophysical testing and it had the lowest value (less than 0.1 s) in the northeastern parts of the city near a bedrock outcrop. The observed damage distribution was also in close agreement with the computed strong ground motion parameters at the ground surface, clearly showing the impacts of local site conditions.

With the maturity and development of digital and intelligent technologies, the traditional construction industry is undergoing changes. However, most construction enterprises in China still lack comprehensive understanding of intelligent construction technologies (Minoli et al. IEEE Internet Things J 4:269–283, Minoli et al., IEEE Internet Things J 4:269–283, 2017). Therefore, research on the application and promotion of ICT is needed. This study aims to identify the factors hindering expressway construction enterprises from adopting ICT. Expert interviews and scoring were used to collect data from 32 experts. The TOSE framework identified fourteen influencing factors. The Fuzzy-DEMATEL-ISM method analyzed the expert scoring results. The results determined that the fourteen hypotheses were established, and the factors had hindering effects on enterprises adopting ICT. This study enhances research rigor by using empirical methods. The TOSE framework allows comprehensive identification of factors. And this study focuses on expressway construction enterprises, an under-researched area.

Accurate identification of high fuel consumption driving behaviors provides good theoretical support for eco-driving training. To gain a deeper understanding of contributing factors and their impacts on fuel consumption, this study acquired a driving data set based on a driving simulator test and employed the light gradient-boosting machine (LightGBM) algorithm to identify driving behaviors related to high fuel consumption and the SHapley Additive exPlanations (SHAP) algorithm for causal analysis. The LightGBM algorithm learns the intrinsic connection between the input variable X and the output variable Y and examines the learning effect. The SHAP algorithm analyzes how the output variable changes with the input variable from different perspectives. First, the vehicle kinematics and fuel consumption data were collected and preprocessed. Secondly, the LightGBM algorithm was employed to classify fuel consumption levels, including low, medium, and high. Thirdly, several evaluation metrics, precision, recall, and F1-score, were used to evaluate the identification results comprehensively, whereas SVM and XGBoost algorithms were employed for comparison. The results show that the LightGBM algorithm significantly outperforms SVM and XGBoost algorithms in precision, recall, and F1-score, respectively. The results show that the LightGBM algorithm performs well in terms of precision, recall, and F1-score. Finally, the SHAP algorithm was used to interpret the influence of contributing factors on high fuel consumption from three perspectives, global interpretation, interaction interpretation, and individual interpretation. The SHAP algorithm can intuitively display the relationship between high fuel consumption and its contributing factors. Specifically, acceleration, speed, roll speed, pitch speed, and engine speed significantly increased the probability of high fuel consumption. This study proposed an efficient combined method for high fuel consumption identification and interpretation, which can reduce the occurrence of high fuel consumption driving behavior, thus achieving the purpose of eco-driving training.

The endeavor of civil and geotechnical engineers to enhance soil stability and durability, reduce settlement, and optimize construction costs is a considerable challenge. Given the intricate nature of these complexities, it is important to note the increasing recognition of ground improvement methods, particularly the use of geosynthetics for reinforcement. Considering these factors, a detailed series of model experiments was conducted to explore the intricate dynamics of load-settlement relationships. This study involved experiments to examine the effects of various geogrid placements and lime content on the mechanical properties and settlement behavior of silty sand reinforced with a single layer of geogrid. Additionally, this research introduces novel computational approaches, specifically artificial neural network (ANN) and extreme learning machine (ELM) models, which utilize evolutionary algorithms and artificial intelligence (AI). These techniques are employed to predict the soil’s load-bearing capacity. Incorporating computational models offers an advanced methodology for predicting the ultimate bearing capacity (UBC) of circular footings in a straightforward and cost-effective manner. The accuracy of these computational models was assessed using well-established statistical measures. The results indicate that the artificial neural network (ANN) model surpasses the extreme learning machine (ELM) model in estimating the ultimate bearing capacity (UBC) of circular footings. This study makes a significant contribution to the field by improving our understanding of soil behavior under various conditions, thus providing crucial insights for enhancing the efficiency and reliability of foundation design.

Brittleness poses a significant threat to the durability of cement-based materials over time. The simultaneous addition of pozzolans and fibers offers a novel and eco-friendly approach to tackle concerns regarding brittleness and environmental impacts associated with conventional cement concrete. This paper investigates the effects of pozzolanic substitutions such as silica fume (SF) and metakaolin (MK) for Ordinary portland cement on the mechanical and toughness performances of steel fiber reinforced concrete (SFRC). In the first part of the study, reference plain concrete mix with a water-to-binder ratio of 0.4 is mixed with different percentages of steel fibers with varying geometry, such as crimped steel (CS) and straight steel (SS) fibers, both as individual and hybrid combinations, to determine the mechanical properties. In the second part, the study evaluated how combining CS and SS fibers influence flexural toughness, aiming to identify combinations that may synergistically enhance performance. The study also examined the influence of pozzolans on the flexural toughness of hybrid steel fiber reinforced concrete (Hy-SFRC). An increase in workability was observed due to the hybridization of steel fibers. The increase in compressive strength, modulus of rupture, and modulus of elasticity was greater in pozzolanic SFRC compared to non-pozzolanic SFRC. The ternary mix of SF and MK showed 18.5%, 91%, and 18.7% improvement in 28-day compressive strength, flexural strength and modulus of elasticity compared to the reference mix. The hybrid combination of CS 1.5% and SS 0.5% was considered the best in terms of mechanical properties. The equivalent flexural toughness results indicated that, both binary and ternary pozzolanic Hy-SFRC was higher compared to non-pozzolanic Hy-SFRC, which can only be mapped to the stronger fiber-matrix bond. Also, the synergy quantification revealed that hybridization was most effective in the post-cracking stages. Hy-SFRC containing the ternary mixture of SF and MK provided a maximum toughness of 61.49 J measured up to L/150. Hy-SFRC mix containing a ternary pozzolanic combination of SF 10% and MK 10% gave the best results in flexural toughness, and the corresponding synergy values were found to be the maximum.

To investigate the seismic performance of a steel frame-braced structural system filled with aluminum foam, the static eccentric and axial compression experiments were performed respectively for square and round steel tubes filled with aluminum foam to obtain a failure deformation diagram and load–displacement curve. Based on the experimental study, a finite element model was established to analyze the seismic performance of the steel frame-braced structural system filled with aluminum foam influenced by different bracing slenderness ratios and diameter-to-thickness ratios. The results show that the failure deformation of the square and round steel tubes filled with aluminum foam was principally constrained by aluminum foam to significantly prevent premature local or whole buckling of the specimen and excellently improve the vertical bearing capacity. The vertical bearing capacity of the pure square steel tube and that filled with aluminum foam under the same loading eccentricity of 50 mm and section size was primarily provided by the steel tube in the elastic stage, and the beneficial effect of infilled aluminum foam was tiny. The ultimate loads of the aluminum foam-filled square steel tubes under the static eccentric experiment with a loading eccentricity of 30 mm, 50 mm and 150 mm were 52.2 kN, 42.1 kN and 10.1 kN respectively. The failure mode of the aluminum foam-filled round steel tubes with specimen lengths and thicknesses increasing respectively from 144 to 336 mm and 1.8 to 3 mm transformed from symmetric folding at the loading end to local buckling at the middle of the specimen under axial load, and the ultimate load of the filler was prominently increased by diminishing the specimen length and increasing the specimen thickness. The increasing rate of ultimate bearing capacity for the aluminum foam-filled steel frame-braced structural system of the bracing with a slenderness ratio of 80 and a diameter thickness ratio of 30 was increased by 73.3% compared to the pure steel frame-braced structural system, and the specimen with the slenderness ratio of 100, 120 and 150 and the diameter thickness ratio of 120 was raised respectively by 65.7%, 37.6% and 43.0%. Infilling aluminum foam remarkably reduced the maximum stress value of the bracing to improve the buckling phenomenon and stress concentration phenomenon and enhance the structure's secant stiffness and energy dissipation capacity. The slenderness ratio of the bracing was recommended to be 80–100 when mainly considering the bearing performance, and the diameter-to-thickness ratio was referred to be 30–40 and 100–120 when the slenderness ratio was 80 and 100, respectively.