| 1. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 3. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
- Khan, Adil, et al.
(författare)
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Predictive modeling for depth of wear of concrete modified with fly ash : A comparative analysis of genetic programming-based algorithms
- 2024
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.
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| 5. |
- Khan, Majid, et al.
(författare)
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Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
- 2023
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Ingår i: Heliyon. - : Cell Press. - 2405-8440. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.
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| 6. |
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| 7. |
- Ahmed Waqas, Hafiz, et al.
(författare)
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Performance Prediction of Hybrid Bamboo-Reinforced Concrete Beams Using Gene Expression Programming for Sustainable Construction
- 2023
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Ingår i: Materials. - : MDPI. - 1996-1944. ; 16:20
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Tidskriftsartikel (refereegranskat)abstract
- The building and construction industry’s demand for steel reinforcement bars has increased with the rapid growth and development in the world. However, steel production contributes to harmful waste and emissions that cause environmental pollution and climate change-related problems. In light of sustainable construction practices, bamboo, a readily accessible and ecofriendly building material, is suggested as a viable replacement for steel rebars. Its cost-effectiveness, environmental sustainability, and considerable tensile strength make it a promising option. In this research, hybrid beams underwent analysis through the use of thoroughly validated finite element models (FEMs), wherein the replacement of steel rebars with bamboo was explored as an alternative reinforcement material. The standard-size beams were subjected to three-point loading using FEMs to study parameters such as the load–deflection response, energy absorption, maximum capacity, and failure patterns. Then, gene expression programming was integrated to aid in developing a more straightforward equation for predicting the flexural strength of bamboo-reinforced concrete beams. The results of this study support the conclusion that the replacement of a portion of flexural steel with bamboo in reinforced concrete beams does not have a detrimental impact on the overall load-bearing capacity and energy absorption of the structure. Furthermore, it may offer a cost-effective and feasible alternative.
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| 8. |
- Al Azzawi, Tiba Nazar Ibrahim, et al.
(författare)
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Evaluation of Iraqi Rice Cultivars for Their Tolerance to Drought Stress
- 2020
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Ingår i: Agronomy. - : MDPI. - 2073-4395. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- Drought stress is a serious problem around the globe and particularly in the Republic of Iraq. Rice is the third most consumed crop for the Iraqi people; however, its cultivation and production is very low due to several challenges including drought. The current study was performed to evaluate five Iraqi rice cultivars along with relevant (drought-tolerant and drought-susceptible) controls under drought stress, either by treatment with 10% PEG (polyethylene glycol) or through water withholding to induce natural drought stress. The phenotypes of all the cultivars were evaluated and the transcriptional responses of key drought-responsive candidate genes, identified through the EST-SSR marker-based approach, were studied. We also studied transcript accumulation of drought-related transcriptional factors, such as OsGRASS23, OsbZIP12, and OsDREB2A. Moreover, the reference cultivars also included a drought-tolerant inter-specific cultivar Nerica 7 (a cross between Oryza sativa ssp. indica X O. glaberrima). Among the cultivars, the more drought-tolerant phenotypic characteristics and higher transcript accumulation of drought-related marker genes OsE647 and OsE1899 and transcriptional factors OsGRASS23, OsbZIP12, and OsDREB2A were observed in four (out of five) significantly drought-tolerant Iraqi cultivars; Mashkab, followed by Furat, Yasmen, and Amber 33. On another note, Amber Barka was found to be significantly drought susceptible. Mashkab and Amber Barka were found to be the most drought-tolerant and-susceptible cultivars, respectively. The identified tolerant cultivars may potentially serve as a genetic source for the incorporation of drought-tolerant phenotypes in rice.
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| 9. |
- Bilal, Muhammad, et al.
(författare)
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Exploring the potential of ligninolytic armory for lignin valorization : A way forward for sustainable and cleaner production
- 2021
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Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526 .- 1879-1786. ; 326
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Forskningsöversikt (refereegranskat)abstract
- Lignin is a key structural constituent of lignocellulosic biomasses that have substantial untapped potential to substitute environmentally unfriendly and non-renewable fossil-based resources. Unfortunately, multifaceted composition, heterogeneity, and structural recalcitrance of the lignin are the biggest technical challenges for its effective deconstruction and bioconversions to an array of bio-based products, e.g., specialty chemicals and biomaterials. Physicochemical methods for lignin depolymerization require strict reaction conditions, high en-ergy to execute processes, and environmental apprehensions. In contrast, biological platforms harnessing the catalytic potentiality of microorganisms and their robust enzymatic armory are thought to be efficient means for lignin decomposition. Enzymes, derived from natural origin, are highly proficient and eco-friendly biocatalysts that manifest high selectivity, require milder reaction conditions, and reduce resource requirements. The utili-zation of enzymes for lignin conversion and pre-treatment of biomass for detergent, textile, pulp and papers, and food sector applications has been investigated for decades. Herein, we reviewed lignin bioconversion by bio-logical means, focusing on ligninolytic enzyme-assisted pretreatment approaches. In the first half, we outlined the lignin as a multipurpose raw feedstock, fixation of CO2 to lignin biosynthesis and tailored lignin approach, and sources and types of lignin. The bio-based pre-treatment approaches for lignin depolymerization, including white-rot fungi, brown-rot fungi, bacteria, and ligninolytic enzymes, i.e., manganese peroxidase (MnP) lignin peroxidase (LiP), Laccase (Lac), versatile peroxidase (VP), and dye-decolorizing peroxidases (DyP) are thor-oughly vetted in the second half.
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| 10. |
- Shafeeque, Muhammad, et al.
(författare)
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Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown : a case study of South Asia
- 2021
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Ingår i: Geomatics, Natural Hazards and Risk. - UK : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 560-580
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Tidskriftsartikel (refereegranskat)abstract
- The strict lockdown measures not only contributed to curbing the spread of COVID-19 infection, but also improved the environmental conditions worldwide. The main goal of the current study was to investigate the co-benefits of COVID-19 lockdown on the atmosphere and aquatic ecological system under restricted anthropogenic activities in South Asia. The remote sensing data (a) NO2 emissions from the Ozone Monitoring Instrument (OMI), (b) Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and (c) chlorophyll (Chl-a) and turbidity data from MODIS-Aqua Level-3 during Jan–Oct (2020) were analyzed to assess the changes in air and water pollution compared to the last five years (2015–2019). The interactions between the air and water pollution were also investigated using overland runoff and precipitation in 2019 and 2020 at a monthly scale to investigate the anomalous events, which could affect the N loading to coastal regions. The results revealed a considerable drop in the air and water pollution (30–40% reduction in NO2 emissions, 45% in AOD, 50% decline in coastal Chl-a concentration, and 29% decline in turbidity) over South Asia. The rate of reduction in NO2 emissions was found the highest for Lahore (32%), New Delhi (31%), Ahmadabad (29%), Karachi (26%), Hyderabad (24%), and Chennai (17%) during the strict lockdown period from Apr–Jun, 2020. A positive correlation between AOD and NO2 emissions (0.23–0.50) implies that a decrease in AOD is attributed to a reduction in NO2. It was observed that during strict lockdown, the turbidity has decreased by 29%, 11%, 16%, and 17% along the coastal regions of Karachi, Mumbai, Calcutta, and Dhaka, respectively, while a 5–6% increase in turbidity was seen over the Madras during the same period. The findings stress the importance of reduced N emissions due to halted fossil fuel consumption and their relationships with the reduced air and water pollution. It is concluded that the atmospheric and hydrospheric environment can be improved by implementing smart restrictions on fossil fuel consumption with a minimum effect on socioeconomics in the region. Smart constraints on fossil fuel usage are recommended to control air and water pollution even after the social and economic activities resume business-as-usual scenario.
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