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

The dynamic mechanical response of functional gradient graphene nanoplatelets reinforced composite arches (GPLs) under the influence of rectangular pulse loading has been studied by employing finite element analysis (FEA). By comparing the static buckling behavior of arches with the peak of the dynamic displacement response, a new method to determine the critical dynamic buckling load and load holding time was established. The specific effects of the distribution pattern, shape and size, mass fraction, and load holding time of GPLs on the dynamic response performance of arches were deeply investigated through a parametric study. It is found that even a small amount of GPLs addition can significantly elevate the dynamic buckling load carrying capacity of arches, and the X-shaped GPLs distribution pattern is the most effective way to enhance the dynamic stability performance of arches. With other parameters being constant, a larger surface area and thinner GPL can improve the performance of the material more significantly.

The evolution of high performance geopolymer concrete (GPC) has become additionally significant for researchers and industry professionals due to the environmental issues related with the use of bulk cement in construction projects. By using fly ash (FA), bottom ash (BA), slag (GGBS), rice husk ash (RHA), and other industrial wastes as the principal binder instead of Portland cement, these mixes promote a greener approach to bulk concrete production. These high-performance blends are often associated with the incorporation of nanomaterials (NM) in the mix. Moreover, it has been demonstrated that NM incorporation offers GPC blends superior mechanical properties, and frequently does away with the requirement for thermal curing which further reduces the energy demand for production. This incorporation of NM also results in a denser inter-particle packing at a micro level, which increases the blend’s durability. The performances of GPC blends dosed with various NM, such as carbon nanotubes (CNT), nano-silica (NS), nano-alumina (NA), nano-titanium di oxide (NT), nano-clay (NC), and nano-graphene oxide (NG), are thoroughly summarized in this article in terms of mechanical, durability, and microstructural qualities. The final inferences and conclusions were drawn keeping in mind the viability of bulk consumption. Ultimately, TOPSIS analysis was carried out to determine the optimum type and dosage of NM in GPC and it was found that NS dosed at 2% yielded the most favorable outcomes. Present limitations and challenges related to the bulk utilization of GPC doped with NM are also discussed towards the end of this review, along with potential directions for further research.

In this paper, uniaxial compression tests were carried out on nine concrete specimens with different coarse aggregate particle size ranges in single and continuous gradation at curing ages of 3, 7 and 28 d, respectively. The acoustic emission (AE) and microseismic (MS) signals of the whole loading process were monitored by combining AE and MS to investigate. The AE and MS signals characteristics of concrete with different coarse aggregate gradations during load damage at different curing ages were analyzedand the effect of the maximum coarse aggregate size on the damage evolution of concrete were investigated. Based on findings from experiments, as the maximum particle size of the coarse aggregate increases, the initial defects within the concrete specimens initially decrease and then increase. A larger size and the more heterogeneous of the coarse aggregate has an accelerating effect on the emergence and expansion of cracks throughout the load-induced damage process in concrete structures. In addition, when the curing age is brief, the expansion of internal cracks in concrete predominantly depends on the curing age, with the relationship to the maximum particle size of coarse aggregate being less evident.

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.

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.

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.

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.