Optimization of constituent proportions for compressive strength of sustainable geopolymer concrete: A statistical approach

Geopolymer concrete (GPC) is an environmentally friendly and sustainable concrete produced through the geopolymerization process, involving the combination of alumino silicate materials with alkaline solutions. It has unique advantage of being carbon-negative which helps reduce carbon dioxide levels in the atmosphere. One challenge with making GPC is the significant variation in compressive strength that arises from differences in constituent proportions, specimen ages, andcuring conditions. There is a need to optimize the GPC constituents to achieve the desired compressive strength. To address this issue, a comprehensive dataset comprising 1242 data points from existing literature on fly ash-based GPC (FA-based GPC) was compiled for the purpose of optimizing the constituent proportions of GPC using statistical analysis. Nine independent variables were selected to develop the predictive models for compressive strength using linear (LRA), multilinear (MRA), and non-linear (NRA) regression analyses. The models were assessed using scatter plots between experimental and predicted responses and adopting the performance criteria such as the statistical significance of the model unknowns, R2 values, root mean square error (RMSE), mean absolute error (MAE), and scatter index (SI). LRA revealed that the compressive strength of GPC is not dependent on any single independent variable. Therefore regression analysis with multiple independent variables was conducted. From MRA, ±15 % error for training dataset and (15–20) % error for validating dataset was estimated. The R2, RMSE, MAE, and SI values were found to be 0.8086, 6.11, 5.35, and 0.21. NRA predicted the compressive strength more efficiently because it provides the error ±10 % for both training and validating datasets with R2, RMSE, MAE, SI values of 0.8321, 5.46, 4.77, and 0.19. Further, curing temperature, specimen age, Na2SiO3/NaOH ratio, I/b ratio, and aggregate content were found as the most significant independent variables. The time and cost-effective design mix for FA based GPC can be prepared using the regression models presented in the current work

Analysis of communication tower with different heights subjected to wind loads using TIA-222-G and TIA-222-H standards

Due to advancements in telecommunications, towers need special attention in terms of the analysis and design under wind loads. The Telecommunications Industry Association (TIA) in 2005 released a standard “TIA-222-G” which has gained a widespread reference for the analysis and design of communication towers. In 2018, TIA released the latest standard TIA-222-H. The latest TIA-222-H standard has some additional features, e.g. limit states for analysis of mounting systems, enhanced climber safety requirements, construction-related loading, etc. To date, not many studies are available describing how much change in member axial forces occurs with the tower height while using the latest standard for analysis. This study’s main objective is to provide guidelines for wind load calculation on tower body, appurtenances, and other structures and compare the member axial forces induced by the wind loads on different tower heights (40, 60, and 80 m) as per TIA-222-G and TIA-222-H standards. The procedure presented in the paper about the design calculations of wind load is a useful guide for structural engineers involved in the analysis and design of communication towers. The analysis results showed that the member axial forces increased by 22% to 37%, which can assist the practitioner in more optimized design

A Machine Learning Model for the Prediction of Concrete Penetration by the Ogive Nose Rigid Projectile

In recent years, research interest has been revolutionized to predict the rigid projectile penetration depth in concrete. The concrete penetration predictions persist, unsettled, due to the complexity of phenomena and the continuous development of revolutionized statistical techniques, such as machine learning, neural networks, and deep learning. This research aims to develop a new model to predict the penetration depth of the ogive nose rigid projectile into concrete blocks using machine learning. Genetic coding is used in Python programming to discover the underlying mathematical relationship from the experimental data in its non-dimensional form. A populace of erratic formulations signifies the rapport amid dependent parameters, such as the impact factor (I), the geometry function of the projectile (N), the empirical constant for concrete strength (S), the slenderness of the projectile (λ), and their independent objective variable, X/d, where X is the penetration depth of the projectile and d is the diameter of the projectile. Four genetic operations were used, including the crossover, sub-tree transfiguration, hoist transfiguration, and point transfiguration operations on supervised test datasets, which were divided into three categories, namely, narrow penetration (X/d < 0.5), intermediate penetration (0.5 ≤ X/d < 5.0), and deep penetration (X/d ≥ 5.0). The proposed model shows a significant relationship with all data in the category for medium penetration, where R2 = 0.88, and R2 = 0.96 for deep penetration. Furthermore, the proposed model predictions are also compared with the most commonly used NDRC and Li and Chen models. The outcome of this research shows that the proposed model predicts the penetration depth precisely, compared to the NDRC and Li and Chen models

Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman

Managing sediments dredged from reservoirs of recharge dams is an environmental issue, however, these sediments can be an abundant and economical source of fine-grained fill soil. This experimental investigation quantifies the geotechnical properties of a reservoir sediment used to improve engineering properties of a poorly graded dune sand in Oman. The binary mixes were prepared with different percentages (5, 10, 20, 50, 75, 90, 95%) of sediment with sand. Laboratory tests such as gradation, consistency limits, compaction, and unconfined compression tests were performed to measure the engineering characteristics of the binary mixtures. The results showed that the maximum dry density increases up to a sediment content of 50% and then decreases with further increase in the sediment content. The optimum water content increases with the increase in sediment content from 17% for pure sand to 22.5% for pure sediment. The optimum moisture content shows a good correlation with the plastic limit of the binary mixture of sand and sediment. The unconfined compressive strength substantially increases with sediment content up to 75% and then decreases with further increase in the sediment content. The binary mixture of sand sediment is sensitive to moisture, however, the order of strength stability against moisture is dune sand mixed with 75, 50, and 20% sediments. The addition of sediment to dune sand improved the uniformity coefficient to some extent with an increase in the maximum and minimum void ratios as well. The elemental analysis of the sediment confirms that the material is non-contaminated and can be employed in geotechnical engineering applications as a sustainable and environmentally friendly solution

Vane shear strength of bio-improved sand reinforced with natural fibre

In the recent developments, bio-improvement has considerably enhanced the geotechnical properties and reduced degradation of loose desert sand without the environmental concerns. However, the effect of bio-improvement on sand weakens due to water submergence. Therefore, it is necessary to explore the techniques for retaining the strength of bio-improved sand in water submerged state. So, this paper reports the geotechnical performance of submerged bio-improved sand reinforced by discrete natural fibres. The desert sand sampled from Al-Sharqia Desert of Oman, bio-improved by xanthan gum and reinforced by discrete natural fibres obtained from date palm tree. Date palm fibres having a diameter less than 0.2mm and length 1cm were employed for sand reinforcement. Vane shear tests and standard compaction tests were performed on specimens having different mix ratios of sand-xanthan gum with and without natural fibres. The qualitative description of bio-improvement and fibre reinforcement effect on the desert sand is also presented as the photomicrographs taken by field emission scanning electron microscope. According to the results of strength tests at higher moisture content state, the discrete fibre reinforcement has enhanced the geotechnical performance of sand improved at lower concentrations

Experimental study on strength and endurance performance of burnt clay bricks incorporating marble waste

Burnt clay brick is one of the oldest and most widely used construction materials. The production of burnt clay bricks with various waste materials can help reduce the environmental hazards and improve brick performance at low manufacturing costs, thereby leading towards more sustainable construction. This research aimed to evaluate the effect of using waste marble powder (WMP) in varying percentages, i.e., 0, 3, 6, 9, 12, and 15%, by weight of clay in an industrial brick kiln plant. A range of mechanical and durability tests was performed on the raw material, i.e., clay, WMP, and bricks, to quantify their performance. It was observed that incorporation of WMP resulted in a reduced unit weight of the bricks, making the structure lighter in weight. Moreover, compressive strength and freeze thaw test results for all the brick specimens and sulfate tests for the brick specimens with 12% WMP addition were within the Building Code of Pakistan, and ASTM C67 prescribed limits. Finally, it can be concluded that WMP up to 12% by weight of clay can be incorporated to prepare burnt clay bricks, which can reduce the environmental waste to achieve sustainability and economy for the brick industry

Computationally efficient GPU based NS solver for two dimensional high-speed inviscid and viscous compressible flows

In this study, we proposed a novel GPU-based solution for modelling two-dimensional inviscid and viscous compressible supersonic/hypersonic flows. Texture and surface pointers are used to access GPU memory locations. For effective and efficient use of surface pointers, we grouped multiple 2D arrays referenced and indexed by a single 3D surface pointer. To enable the proposed solver for double-precision calculations, two consecutive 32-bit memory locations were grouped to maintain the efficiency of surface pointers while taking advantage/accuracy of 64-bit calculations. Resolving data and computation dependencies for parallel applications is another complex task that is the focus of this study. Computation dependencies have been solved by using multiple mutually synchronized GPU kernels and executing them sequentially using the GPU default stream to ensure that all relevant data is available to the threads or computed before they actually use it. Consequently, there is no intra-core data dependency in our proposed approach, while inter-core data dependency is successfully solved by stringing multiple kernels together. Using NVIDIA GTX 660 GPUs, we achieved 20x speedup compared to traditional Core i5[Formula: see text] computers. This speedup is the result of the Surface Pointer's GPU capabilities for double precision computations. The simulation results are also consistent with the experimental and numerical results of this study