•Initial and final setting times of the mixes increase with the seashell ash substitutions.•The spreadability and density properties decrease.•The MSA ash-based LFC exhibits lower capillary sorption, ...water absorption, and gas permeability.•It was obtained that the 15% MSA replacement in LFC exhibited 15.89%, 17.95% and 12.41% improvement in flexural, split-tensile, and compressive strength.•Moreover, porosity, SEM assessment, and pores distribution indicated that up to 15% replacement, the seashell as-based concrete performed better than the rest of the substitutions.•The thermal conductivity, thermal diffusivity, and specific heat capacity exhibited better performance of sea shell-based specimens.
The comprehensive properties of waste seashell ash (Oyster shell ash (OSA), Scallop shell ash (SSA), and Mussel shell ash (MSA)) in lightweight foamed concrete (LFC) are needed to explore for using these aquaculture wastes in light-weight building structures. Therefore, this study’s aim is to investigate the fresh, transport, mechanical, pore structure, and thermal properties of OSA, SSA and MSA-based LFC. The seven LFC mixes have been prepared to evaluate the effect of 5, 10, 15, 20, 25, and 30% cement replacement by seashell ash and compared with the control mix. The test findings show that mixes' initial and final setting times increase with the seashell ash substitutions; however, the spreadability and density properties of the fresh test decrease. The MSA ash-based LFC exhibits 0.29–1% lower water absorption than the OSA and SSA ash-based specimens. It was further observed that the 15% MSA replacement in LFC exhibited a maximum of 12.41%, 15.89% and 17.95% enhancement in compressive, flexural and splitting tensile strength compared to the reference mix. Moreover, SEM assessment and pores distribution indicated that up to 15% replacement, the seashell as-based concrete performed better as a pore filler than the rest of the substitutions. The thermal conductivity and specific heat capacity exhibited better performance for all sea shell-based specimens; however, a 15% replacement of MSA seashell-based concrete can be the optimum solution for eco-friendly concrete production considering the overall assessment.
In order to reduce the CO2 associated with cement production, this study explored the potential of rice husk ash (RHA) and fly ash (FA) as supplementary cementitios materials for partially replacing ...cement in concrete production. The study aimed to analyze the synergistic effects of a cement-based mixture consisting of RHA and FA in different proportions on concrete's fresh, hardened, non-destructive and microscopic properties. In addition to the experimental work, this study successfully applied machine learning to predict the compressive strength of RHA-FA concrete using three types of algorithms: ANN (Analytical Neural Network), XGB (Extreme Gradient Boosting), and GBM (Gradient Boosting Model). A total of 138 data points were used for this prediction, and statistical and parametric analyses were performed to define the impact of input parameters on the outcome. Furthermore, both destructive and non-destructive tests were conducted on hardened concrete, including compressive strength, split tensile strength, ultrasonic pulse velocity (UPV), and rebound hammer. The scanning electron microscope (SEM) was operated to analyze the microstructural characteristics of concrete. The compressive and split-tensile strength test results showed that the mixture with a higher percentage of fly ash and a lower percentage of rice husk ash achieved maximum strength. The X-ray Fluorescence (XRF) analysis revealed that both ashes contained a significant amount of silica, which gave them excellent pozzolanic properties. After 28 days, both the UPV and the rebound hammer strength align with the destructive compressive strength results. The study also employed SHAP (SHapley Additive exPlanations) and PDP (Partial Dependence Plot) analyses to identify the optimal range for each parameter's contribution to strength improvement. The machine learning models exhibited a strong correlation with the test results, achieving an R2 value of 0.84 for the XGBoost model.
The use of plastic bottles is on the rise, causing lots of new environmental issues. It is now extremely difficult to dispose of plastic waste without damaging the ecosystem. As black cotton soil for ...embankments and foundations has limited use due to its swelling and shrinkage property as well as its bearing capacity, utilizing plastic bottles to enhance soil strength parameters can be a cost-effective solution. This study investigates the possibility of improving the characteristics of soil by using leftover strips of plastic that have been cut from plastic water bottles. The black cotton soil is amended with about 0%, 5%, 6%, 7%, 8%, 9%, and 10% plastic strips. On the black cotton soil that had been strengthened with plastic waste, direct shear and falling head water permeability tests were executed as part of the experiment. The test results under the circumstances are provided to evaluate the change in soil strength characteristics. In the aspects of shear strength, friction angle, and cohesiveness, it was determined that soil samples containing 5-9% plastic strips performed better than soil samples containing no plastic strips. Therefore, shear strength can assist in resisting deformation under stress caused by the weight of an embankment. Moreover, the friction angle determines the embankment slope stability and the material's ability to withstand deformation caused by water flow, freeze-thaw cycles, or other environmental factors.. On the other hand, the inclusion of the plastic strip enhanced the permeability of black cotton-reinforced soil. This innovative approach may be utilized to successfully address socioeconomic issues, such as recycling waste materials.
The construction industry is making efforts to reduce the environmental impact of cement production in concrete by incorporating alternative and supplementary cementitious materials, as well as ...lowering carbon emissions. One such material that has gained popularity in this context is rice husk ash (RHA) due to its pozzolanic reactions. This study aims to forecast the compressive strength (CS) of RHA-based concrete (RBC) by examining the effects of several factors such as cement, RHA content, curing age, water usage, aggregate amount, and superplasticizer content. To accomplish this, the study collected and analyzed data from literature, resulting in a dataset of 1404 observations. Several machine learning (ML) models, such as light gradient boosting (LGB), extreme gradient boosting (XGB), and random forest (RF), as well as hybrid machine learning (HML) approaches like XGB-LGB and XGB-RF were employed to thoroughly analyze these parameters and assess their impact on strength. The dataset was split into training and testing groups, and statistical analyses were performed to determine the relationships between the input parameters and CS. Moreover, the performance of all the models was evaluated using various statistical evaluation criteria, including mean absolute percentage error (MAPE), coefficient of efficiency (CE), root mean square error (RMSE), and coefficient of determination (R2). The hybrid XGB-LGB model was found to have higher precision (R2 = 0.95, and RMSE = 5.255 MPa) as compared to other models. SHAP (SHapley Additive exPlanations) analysis revealed that cement, RHA, and superplasticizer had a positive effect on strength. Overall, the study's findings suggest that the hybrid XGB-LGB model with the identified input parameters can be used to accurately predict the CS of RBC. The application of such technologies in the construction sector can facilitate the rapid and low-cost identification of material qualities and the impact of input parameters.
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Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced construction material known for its exceptional mechanical properties and durability. Recently, machine learning (ML) methods ...have played a pivotal role in predicting the compressive strength (CS) of UHPC and evaluating the dominant input parameters for a suitable mix design. In this research, three hybrid machine learning models were utilized: Random Forest (RF), AdaBoost (AB), and Gradient Boosting (GB) algorithms with particle swarm optimization (PSO), namely AB-PSO, RF-PSO, and GB-PSO, to predict compressive strength and perform SHAP (Shapley additive explanation) analysis. To build predictive hybrid ML models, a dataset of 810 experimental data points was collected for compressive strength (CS) from published literature. Additionally, SHAP interaction plots were generated to visualize the impact of each feature on a specific prediction made by the models. The results indicated that hybrid machine learning models performed better than traditional models, and the hybrid GB-PSO model showed the high prediction accuracy among models. The hybrid GB-PSO model had higher precision compared to the other two models. Hybrid GB-PSO model achieved R2 values of 0.9913 during the training stage and 0.9804 during the testing stage for the prediction of CS. The SHAP analysis revealed that age, fiber, cement, silica fume, and superplasticizer had a significant influence on compressive strength, while the impact of other input parameters was comparatively lower. The PDP (Partial Dependence Plots) analysis results amount of individually input variables material can be calculated simply for the designed CS. These findings are valuable for construction applications and offer essential insights for design engineers and builders, aiding their understanding of the significance of each component in UHPC.
The incorporation of waste materials generated in many industries has been actively advocated for in the construction industry, since they have the capacity to lessen the pollution on dumpsites, ...mitigate environmental resource consumption, and establish a sustainable environment. This research has been conducted to determine the influence of different rice husk ash (RHA) concentrations on the fresh and mechanical properties of high-strength concrete. RHA was employed to partially replace the cement at 5%, 10%, 15%, and 20% by weight. Fresh properties, such as slump, compacting factor, density, and surface absorption, were determined. In contrast, its mechanical properties, such as compressive strength, splitting tensile strength and flexural strength, were assessed after 7, 28, and 60 days. In addition, the microstructural evaluation, initial surface absorption test, = environmental impact, and cost-benefit analysis were evaluated. The results show that the incorporation of RHA reduces the workability of fresh mixes, while enhancing their compressive, splitting, and flexural strength up to 7.16%, 7.03%, and 3.82%, respectively. Moreover, incorporating 10% of RHA provides the highest compressive strength, splitting tensile, and flexural strength, with an improved initial surface absorption and microstructural evaluation and greater eco-strength efficiencies. Finally, a relatively lower CO
-eq (equivalent to kg CO
) per MPa for RHA concrete indicates the significant positive impact due to the reduced Global Warming Potential (GWP). Thus, the current findings demonstrated that RHA can be used in the concrete industry as a possible revenue source for developing sustainable concretes with high performance.
This paper studies the fresh and mechanical properties of high-strength concrete (HSC) by incorporating recycled concrete aggregates (RCA) of varying sizes and concentrations. The recycled aggregate ...concrete (RAC) was prepared by partially replacing RCA with natural coarse aggregate (NCA) at 0%, 15%, 30%, and 45%, with aggregate sizes ranging from 5 to 12 and 12–20-mm. Fresh concrete properties, such as slump, Kelly ball, compacting factor, K-slump, and fresh density, were tested to determine the influence of RCA size and concentration. In addition, the mechanical properties were studied through the execution of compressive, split-tensile, and stress-strain tests. The test results revealed that increasing the RCA concentration declines the fresh and hardened properties of HSC. In the fresh concrete experimentation, the 12–20 mm aggregate size RAC mixes exhibited greater workability than the 5–12 mm aggregate mixes. On the contrary, 5–12 mm aggregate mixes RAC had higher compressive and split-tensile strength and a higher modulus of elasticity than 12–20 mm aggregate mixes concrete. When it comes to sustainability, the study found that the smaller size range of RAC produces inferior embodied CO2(eCO2) and provides a cost-effective and sustainable solution for the construction industry.
The construction industry faces many challenges, including schedule and cost overruns, productivity constraints, and workforce shortages. Compared to other sectors, it lags in digitalization in every ...project phase. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies revolutionizing the construction sector. However, a discernible gap persists in systematically categorizing the applications of these technologies throughout the various phases of the construction project life cycle. In response to this gap, this research aims to present a thorough assessment of the deployment of AI and ML across diverse phases in construction projects, with the ultimate goal of furnishing valuable insights for the effective integration of these intelligent systems within the construction sector. A thorough literature review was performed to identify AI and ML applications in the building sector. After scrutinizing the literature, the applications of AI and ML were presented based on a construction project life cycle. A critical review of existing literature on AI and ML applications in the building industry showed that AI and ML applications are more frequent in the planning and construction stages. Moreover, the opportunities for AI and ML applications in other stages were discussed based on the life cycle categorization and presented in this study. The practical contribution of the study lies in providing valuable insights for the effective integration of intelligent systems within the construction sector. Academically, the research contributes by conducting a thorough literature review, categorizing AI and ML applications based on the construction project life cycle, and identifying opportunities for their deployment in different stages.
Infestation by various insect pests is the main constraint for growing rice where rice brown planthopper (Nilaparvata lugens Stål) can severely damage rice plants directly through feeding. Therefore, ...the study aims to detect rice brown planthoppers (BPH) and provide environment-friendly management tactics to mitigate the problem which caused by brown planthoppers. The BPH samples were collected from rice fields of different locations in the Patuakhali of Bangladesh for molecular identification. A molecularly single species of rice brown planthopper, Nilaparavata lugens was identified using mitochondrial cytochrome oxidase subunit I (mtCOI) universal marker. The nucleotide sequences of collected samples were compared with other nucleotide sequences from the GenBank database of NCBI, which make single clades in the phylogenetic tree at an insignificant distance. Moreover, brown planthopper management observations were recorded in laboratory conditions after providing an artificial diet with different treatments of plant-based insecticides Neem oil (1 %, 5 %, and 10 %), Castor oil (1 %, 5 %, and 10 %) where only 20 % sucrose solution was used as negative control and Abamectin (1 %, 5 % and 10 %) were also used as a positive control for comparing the efficacy of plant-based insecticides on rice brown planthoppers. The results showed the highest mortality (100 %) of rice brown planthoppers was recorded by Abamectin 10 %, followed by Abamectin 5 %. Neem 10 % performed better than Abamectin 1 % during 1st hour. Initial after exposure of 2nd hour for Abamectin 1 % revealed greater mortality (59 %) than Neem 10 %. Neem 5 % showed less effect on mortality in brown planthopper than Neem 10 % but was higher than Neem 1 % during 6 h of observation. The Castor oil of 10 % caused higher mortality than the Castor of 5 % but not up to the marks of Abamectin and different concentrations of Neem oil. Castor oil of 1 % and control have shown no mortality of brown planthopper for 6 h of observation.
The innovative utilization of sugarcane bagasse ash (SCBA) in producing lightweight concrete (LWC) needs to be explored experimentally and quantification with machine learning approaches. Therefore, ...this study aims to investigate fresh, mechanical and microstructural characteristics and machine learning modeling of incorporating SCBA as a partial substitute of cement. In this investigation, OPC was replaced partially by the weight of SCBA in the proportion of 0 %, 5 %, 10 %, 15 %, and 20 %. The influences of SCBA on the fresh characteristics of concrete were investigated through tests of the slump, compacting factor, Kelly ball, k-slump, density, and air content. In addition, for hardened properties compressive, splitting tensile, flexural strength, and static modulus of elasticity were tested at 7 and 28 days. Furthermore, a microstructural characteristic was carried out for varying content of SCBA. Different machine learning (ML) predictive models were performed utilizing artificial neural network (ANN) and random forest (RF), to predict the fresh and mechanical attributes of the LWC. It was noticed that the workability value of fresh concrete was enhanced almost by 40 % to 50 % with the incorporation of SCBA content. The hardened test results revealed that a mixture having SCBA up to 10 % content SCBA resulted in nearly 10 % to 30 % better performance to satisfy the requirement of LWC. Regarding the sustainability assessment, the less embodied CO2 (eCO2) is produced in SCBA mixes, whereas it is also economically beneficial to produce SCBA mixes. Models' efficacy was evaluated including mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). The maximum R2 and minimum RMSE came across for RF (0.989 and 1.393, respectively) which provides the well estimation of the model.
