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

Various forms of superhydrophobic carbon-based nanomaterials have been extensively attracted to advanced fields. Although it is widely implemented, its potential environmental impact and uneconomical has limited its utilization. To overcome these shortcomings, this article aimed to provide Carbon-based Sustainable Superhydrophobic (CSS) nanoparticles obtained from the pyrolysis of tyre waste. Firstly, CSS nanoparticles were characterized with microscopic, spectroscopic, and hydrophobic measurements. This article subsequently studied the development of hydrophobic cement composites using CSS nanoparticles and reviewed the advanced progress in the research of surface wettability and surface energy using a time-dependent contact angle measurement technique. Further, the role of CSS nanoparticles in cement composites is examined through mechanical strength and microstructure characterization. The water contact angle results showed that the cement composites with CSS nanoparticles achieved hydrophobic and exhibited the highest contact angle of 132.15º (over-hydrophobic) for 3wt%. The CM-3 mix has the lowest value of total (γ_S), dispersive (({gamma }_{s}^{d})), and polar surface energy (({gamma }_{s}^{p})) of 11.95 mJ/m², 11.63 mJ/m², and 0.324 mJ/m² respectively. Moreover, the compressive and flexural strength improved significantly with the addition of CSS nanoparticles, attaining maximum strength of 55.65 MPa (CM-2) and 7.8 MPa (CM-1.5), respectively. The 3wt% CSS nanoparticles are successfully disseminated with 10% SF, exhibiting a relatively high reduction of capillary absorption. Microstructure investigation shows that CSS nanoparticles are well entangled in SF, resulting in a dense and compacted matrix structure. Therefore, CSS particles will be an advanced and sustainable nanomaterial for developing an integral hydrophobic cement composite.

Graphical Abstract

Basalt fibers offer potential benefits by improving the energy absorption capacity and impact resistance of concrete structures, enhancing their resilience. This research investigates the influence of incorporating basalt fibers on the compressive and flexural strength of concrete, as well as its resistance to impact loads. The study involved casting 15 cylinders (150 × 300 mm) to assess compressive strength, 10 prism beams (100 × 100 × 400 mm) for flexural strength, and 5 large-scale reinforced concrete beams (150 × 150 × 2000 mm) to evaluate impact resistance. Basalt fibers of 18 mm and 38 mm lengths were added in proportions of 0.26% and 0.39% by volume of the mix, separately. Impact resistance was assessed by subjecting the samples to an impact load apparatus, dropping 210 kg weight from a height of 750 mm using a pulley system. The findings demonstrate that incorporating basalt fibers enhances both compressive and flexural strength, as well as impact resistance. Specifically, the addition of 18 mm length basalt fibers at a dosage of 0.26% led to a remarkable 23% increase in compressive strength compared to plain concrete. BFRC samples also exhibited higher flexural strength than plain concrete. In terms of impact resistance, the large-scale beam sample B3, consisting of 38 mm length fibers at a 0.26% content, displayed the least deflection, while the beam without fibers exhibited the most deflection. Overall, all samples reinforced with basalt fibers demonstrated reduced deflection compared to those without fibers.

In the literature, few researchers have investigated the cyclic performance of the low-yield-point (LYP) steel links, and along with the existence of some uncertainty about the strain hardening contribution to the overstrength factor of LYP steel links, there is no analytical spring-based model used in general-purpose structural analysis programs for modeling the actual behavior of the EBFs. Therefore, researchers have to generate a micro finite element (FE) model for evaluating the seismic performance of these types of systems. To provide guidance for engineering applications, this paper tries to develop an experimentally validated numerical model for generating the cyclic backbone curve of LYP links. In this regard, firstly, a comprehensive instruction was established for valid modeling of the LYP steel I-shaped links using ABAQUS software, and then the accuracy of the proposed FE model was assessed by comparing with six available experimental tests, indicating a good agreement between the results in terms of initial stiffness, post-yielding stiffness, ultimate shear strength, deformation capacity, and also damage initiation and evolution.

Geopolymer concrete serves as an eco-friendly substitute for traditional Portland cement-based concrete, notorious for its high carbon footprint due to substantial carbon dioxide emissions during production. Phosphogypsum and ground granulated blast slag are industrial wastes that can be used as an alternative to cement, along with micro silica. Phosphogypsum and ground granulated blast slag can be developed into geopolymer concrete with alkali solutions. This work investigates the replacement of phosphogypsum with ground granulated blast slag and micro silica. For the purpose of the study, strength and durability were tested through mechanical properties, rapid chloride penetration test, water absorption and porosity. The maximum strength achieved was 60.88 MPa in the case of replacing 20% micro silica for phosphogypsum, while this result corresponded to minimal values for rapid chloride penetration test, water absorption, and porosity when phosphogypsum was replaced with 20% micro silica. Moreover, scanning electron microscope images illustrated the gel formation in the geopolymer concrete that contributed to strengthening the samples. Additionally, extreme gradient boosting was also analyzed for statistical means. The R² value of 0.9999 signifies that the extreme gradient boosting accounts for accurate in training cases for compressive strength.

As a supply-side option to manage the depleting groundwater resources in India, the Central Government has made it mandatory to install rain water harvesting system in any building with a plot size of 100 m² (MoUD (Ministry of Urban Development), Model Building Bye-Laws, Town and Country Organization, Government of India, 2016). An attempt has been made in this contribution to carry out feasibility study for rooftop rain water harvesting at the Grey Iron Foundry (GIF), Jabalpur, Madhya Pradesh, India. Since the post-monsoon groundwater levels in the area are in the order of about 2–3 m, scope for gravity recharge is limited and there is not much natural subsurface space available for storage of the harvested rain water. However, underground and on-the-ground artificial tanks can be constructed to store the water for further use. The three buildings examined have a combined roof area of 21,927 m² with a rain water availability of 21,784 m³ giving a recharge potential of about one m³ of rain water for every m² of roof area. Groundwater in the area contains high amount of fluoride and cannot be used for drinking purposes without adequate treatment. It is also not advisable even to drink the harvested rain water since it is slightly acidic in nature. It can, however, be used for other useful purposes, such as for gardening, horticulture and industrial cooling. It is estimated that about 85% of the cooling water requirements of the GIF can be met by harvested rain water. India consists of innumerable buildings across the country, and this foundry serves as a case study to harvest rain water in small scale industrial complexes even if post-monsoon groundwater levels are shallower to save the country from an impending danger.

Leachate generation is a serious problem for groundwater quality in and around municipal solid waste dumpsites. The degradation of leachate water contaminants using TiO₂-doped nanocomposites as photocatalysts is studied. The Fe₂O₃-doped TiO₂ and Cu-doped TiO₂ nanocomposites are synthesized by the sol–gel method to degrade landfill leachate water contaminants. The impregnation of Fe₂O₃ and Cu into TiO₂ aims to enhance the separation and migration of electron–hole pairs, increase the generation of reactive oxygen species, and ultimately improve the efficiency of photocatalytic degradation of contaminants in landfill leachate.SEM, XRD, EDX, FITR, and UV-DRS are used to analyze the surface morphology, particle size, elemental composition, and band gap energy of the prepared photocatalysts. The XRD results show Fe₂O₃/TiO₂ and Cu/TiO₂ have crystalline sizes of 40.03 nm and 18.15 nm, respectively. The surface morphologies of Fe₂O₃/TiO₂ have a non-uniform size and a spherical shape, but Cu/TiO₂ are tiny, spherical, and slightly clustered. The bandgap energies of Fe₂O₃/TiO₂ and Cu/TiO₂ are 3.75 eV and 3.87 eV, respectively. The optimal parameters, such as pH, catalyst dosage, and light intensity, are studied to determine the degradation of chemical oxygen demand (COD) and color removal. Results revealed that in the trapezoidal reactor, the maximum decolorization of 67%, 90%, and 78% and COD removal of 63%, 81%, and 72% are achieved for TiO₂, Fe₂O₃/TiO₂, and Cu/TiO₂ photocatalysts, respectively, at 3.5 h. Similarly, for the cylindrical reactor, color removal of 60%, 80%, and 70% and COD removal of 55%, 72%, and 65% are achieved for TiO₂, Fe₂O₃/TiO₂, and Cu/TiO₂ photocatalysts, respectively, at 3 h. The results of photocatalytic degradation of the leachate ensures the efficiency of the reactor and it is reused for several times to attain the maximum stability for real-time application in the treatment of landfill leachate.

Graphic Abstract

This research aims to advance the construction industry’s progression by examining the complicated dynamics of concrete combined with nano-silica (NS) and ground granulated blast furnace slag (GGBFS), with the fundamental goal of establishing a reliable stress–strain constitutive correlation. The potential of blended concrete with NS (0–5%) and GGBFS (0–25%) as partial cement replacements at temperatures ranging from 27 to 1000 °C was investigated to address critical issues such as fire damage and durability aspects. The results showed an impactful improvement in the stress–strain characteristics within blended concrete by selectively evaluating stress–strain behaviour together with thorough evaluations of compressive strength, elastic modulus, water sorptivity, sulphate resistance, and water absorption. The results appear at 4% NS and 20% GGBFS, yielding better mechanical, resilient, and micro-structural performance at high temperatures. Amidst deterioration, the blended concrete outperformed the control sample, demonstrating the synergistic benefits of NS and GGBFS in creating a more waterproof and long-lasting concrete structure. In the last phase, the correlation between mechanical properties at ambient (27 °C) and increased temperatures was presented to develop a strong stress–strain constitutive model. This model relates the experimental data well, confirming the intricacies of the created concrete blend. This study not only improves the clarity of the observations into concrete performance but also strengthens the application of this study in real-world circumstances, laying the framework for future construction improvements.

To detect structural damage, the static deflections due to a moving load, measured at three different points of the structure, are used with the model updating method. In this method, by minimizing the difference between the responses of the damaged and analytical structures, the location and severity of damage are obtained. A new criterion called ‘deflection influence line indicator’ (DILI) is presented and used as an objective function. Moreover, by integrating the enhanced symbiotic organisms search (ESOS) algorithm and the simulated annealing (SA) algorithm, a new algorithm called the ‘hybrid enhanced symbiotic organisms search-simulated annealing algorithm’ (HESOS-SA) is presented which improves on the original ESOS algorithm. In the proposed algorithm, the global search (exploration) is performed by the ESOS algorithm, whereas the local search (exploitation) is done by the SA algorithm. The original SOS and the proposed HESOS-SA algorithms are used to minimize the DILI criterion. In order to assess the performance of the proposed method for structural damage detection, three benchmark structures, including a simply-supported beam and 25-member and 31-member planar truss problems, with a number of damage scenarios are considered. The numerical results demonstrate that, for noise-free data, both the SOS and HESOS-SA algorithms can correctly detect both the location and severity of damage using the DILI criterion. On the other hand, for noisy data, the HESOS-SA algorithm has a more robust performance in damage detection than the SOS algorithm.

In the quest for sustainable construction practices, researchers have been exploring alternative materials that can reduce the reliance on traditional cement in concrete production. Geopolymer concrete (GPC) has surfaced as a promising alternative due to its potential ecological benefits. The formulation of GPC mixtures is a challenging task as there is no specific code provision to determine the mix design. The complexity of determining the optimal mix proportions is compounded by the influence of various factors, including the Na₂SiO₃/NaOH ratio, the quantities of sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH), and differing curing periods, all of which significantly impact the concrete’s mechanical properties. A variety of predictive modeling techniques, including multivariate adaptive regression splines (MARS), group method of data handling (GMDH), M5P, and linear regression (LR), are used in the estimation of the compressive strength of rubberized slag-based GPC. This study utilizes a dataset comprising 186 observations, which are divided into a training dataset of 130 observations and a testing dataset of 56 observations. The investigation considers various input parameters such as the molarity of NaOH (S), Na₂SiO₃ quantity (SS), sand quantity (S), coarse aggregate quantity (CA), NaOH quantity (M), the quantity of copper slag (C), rubber aggregate quantity (RA), curing period (D), and fly ash (FA), with the compressive strength serving as the output constraint. The efficacy of these approaches is assessed using performance indices such as the coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), and scattering index (SI). The findings indicate that the MARS model outperforms the other soft computing techniques, with a testing CC of 0.9634, MAE of 1.4509, RMSE of 1.8465, SI of 0.0480, and NSE of 0.9265. Conversely, the LR model exhibits the least favourable performance, with testing values of CC at 0.8640, MAE at 3.0411, RMSE at 3.5375, SI at 0.0920, and NSE at 0.7303. These results emphasize the potential of MARS as a suitable method for predicting the compressive strength of rubberized slag-based GPC, leading to more sustainable construction methodologies.

Compressive strength prediction of Basalt Fiber Reinforced Concrete (BFRC), an advanced building material that combines performance and sustainability, is a complex task influenced by many factors. In this study, the compressive strength of BFRC is predicted using four tuned machine learning models, namely, Support Vector Machine (SVR), Random Forest (RF), Back Propagation Neural Network (BPNN), and Extreme Gradient Boosting (XGB), and analyzed using SHAP (Shapley additive approach). To build the machine learning model, a database containing 309 sets of BFRC compressive strength data collected from published articles was established in this study, and an additional 8 sets of BFRC compressive strength data were obtained through experimental work. SHAP interaction plots were generated to explain how the value of each characteristic affects the model prediction, and the optimal range of values for the basalt fiber characteristics was clarified. The results show that the XGB model outperforms the other three models in terms of prediction, with the coefficient of determination (R²) value of 0.9431, the root mean square error (RMSE) of 3.2325, and the mean absolute error (MAE) of 2.3355. Among the three basalt fiber parameters, the volume content of the basalt fibers has the greatest effect on the model output. In addition, the optimal range of volume content was 0.1%, the optimal range of diameter was 15–20 μm, and the optimal range of length was 8–15 mm.