Influence of winter wheat on soil thermal properties of a Paleudalf

Soil thermal properties can influence several soil processes important for crop productivity. This study was conducted to evaluate the influence of cover crops on selected soil physical and thermal properties. The field site was set up using a randomized complete block design with two levels of cover crops (cover crops versus no cover crops). The soil thermal properties measured included thermal conductivity, volumetric heat capacity, and thermal diffusivity. The physical properties of the soil studied included bulk density, volumetric water content, total pore spaces, water-filled pore spaces, air-filled pore spaces, gas diffusion coefficient, and soil pore tortuosity factor. Soil organic carbon was also measured. The results showed that soil organic carbon was 26% higher under cover crops management compared to no cover crops management. Thermal conductivity and thermal diffusivity were positively correlated with soil bulk density and these properties (soil thermal conductivity and soil thermal diffusivity) were higher under no cover crops management compared with cover crops management probably due to the proximity between soil particles. The volumetric heat capacity was positively correlated with soil organic carbon, with soil organic carbon being higher under cover crops management compared with no cover crops management. Results from the current study show that cover crops can improve soil physical and thermal properties which may benefit crop productivity as corroborated via laboratory measurements

Evaluation of bond strength between ultra-high-performance concrete and normal strength concrete: an overview

Ultra-high-performance concrete (UHPC) is an advanced, durable cementitious material with excellent mechanical properties, which makes it an appropriate material for strengthening, repair, and retrofitting of damaged concrete structures. The UHPC as a repair material on normal strength concrete (NSC) depends on the quality of the bond strength at the interface. The interface behavior of UHPC-NSC concrete has a significant impact on its overall durability performance. This paper reviews the studies conducted on the bond strength at the concrete interface to determine the effectiveness of different bond strength testing techniques. The review has shown that the bond strength is commonly evaluated through splitting tensile, slant shear, direct shear, pull-off, bi-surface shear, and third-point flexural tests. Slant shear and splitting tensile test methods are the most common techniques used to evaluate the performance of bond strength between UHPC and NSC. splitting tensile tests, stress is directly applied at the concrete interface, while in the slant shear test, the interface surface is subjected to combined compressive stress and shear stress. Thus, Splitting tensile tests produces more accurate results than the slant shear test. Bi-surface shear and third-point flexural tests are easy to conduct and give a result similar to splitting and direct shear tests. However, further investigations are required on the reliability of the test methods under different conditions. In terms of failure modes, splitting tensile tests produces a more consistent result compared to the pull-off test

Implementation of hybrid neuro-fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: A soft computing technique

Carbonation is one of the critical problems that affects the durability of reinforced concrete; it is a reaction between CO2 gas and Ca (OH)2 when H2O is available, which forms powdery CaCO3 that alters the microstructure of the concrete by reducing its pH level and initiating corrosion that reduces the structure's service life. This study provides experimental information on the carbonation depths of samples from 10 separate existing reinforced concrete structures, where five are located in the inland area (Nicosia), while the other five are in the coastal area (Kyrenia) of the Turkish Republic of North Cyprus. The study found that the inland buildings have a higher depth of carbonation compared to the coastal buildings. The building structures in North Cyprus exhibit a higher rate of carbonation than the expected threshold within their life span. Constant values of B were yielded, which is useful in predicting carbonation depth. Using AI, the potential Hybrid Neuro-fuzzy model, which is comprised of an Adaptive Neuro-fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Machine (SVM) and a Conventional Multilinear Regression (MLR) model, were employed for the estimation of carbonation depth using experimental data, including age, compressive strength, current density, and carbonation constant. Four different performance indexes were used to verify the modelling accuracy, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash- Coefficient (NSE), and Correlation Coefficient (CC). The results indicated that the AI models (ANFIS, ELM, SVM) performed better than the linear model (MLR) with NSE-values higher than 0.97 in both the testing and training stages. The results also indicated that the prediction skills of ANFIS-M2 increased the performance accuracy of ELM-M2, SVM-M2, and MLR-M2, and the ANFIS-M1 model performed better than ELM-1, SVM-1 and MLR-1 models in terms of prediction accuracy. The final outcomes indicated the capability of the non-linear models (ANFIS, ELM, and SVM) in the prediction of Cd

Sensitivity and robustness analysis of adaptive neuro-fuzzy inference system (ANFIS) for shear strength prediction of stud connectors in concrete

The shear strength of stud connectors is essential for designing steel-concrete structures, which is assessed only through a push-out test or available design codes. An alternative technique that eliminates the need to conduct the push-out test is soft computing (SC). The performance of any machine learning (ML) based prediction model depends on the sensitive parameters used in the model development. This paper performs a sensitivity analysis on the shear strength prediction of stud connectors embedded in concrete. A system identification (SI) was conducted using an adaptive neuro-fuzzy inference system (ANFIS) to find the most sensitive combinations of input variables. Six different models were developed based on the SI results. Three machine learning algorithms, including ANFIS, extreme learning machine (ELM), and artificial neural network (ANN), were used to estimate the shear strength of stud connectors in each developed model. The results show that the number of studs (n) is the most sensitive parameter in predicting shear strength. Irrespective of concrete compressive strength (fc), the combination of the stud diameter (ϕ), number of studs, and stud spacing (s) can predict the shear strength with the accuracy of ±8.67 kN. The robustness of the three AI algorithms was evaluated using the Monte Carlo Simulation method. The individual conditional expectation (ICE) was also presented to visualize the correlation between the target shear strength and the six predictors. The results of this study show that sensitivity analysis is an essential tool for any data-driven ML model for accurate prediction

Impact resistance and flexural behavior of U-shaped concrete specimen retrofitted with polyurethane grout

Normal concrete-polyurethane grout (NC-PUG) composite U-shaped and beam specimens were developed in this study to evaluate the capability and efficacy of PU grouting material prepared from mixing bio-based polyurethane (PU) and quartz sand as a coating material. The composite specimens were subjected to dynamic and flexural loads using U-shaped drop-weight impact test (USDWIT) and three-point bending test, respectively. Various PUG overlaid thicknesses and configurations were adopted to improve the impact and flexural behavior of the concrete. Weibull distribution analysis was performed on the USDWIT result. The result indicated that the reference NC-PUG beam exhibited the highest load-carrying capacity of 18.26 kN and the lowest ultimate deflection of 0.52 mm compared to the composite NC-PUG specimens. While, the NC beam retrofitted with a 10 mm thickness overlaid at the top surface (NC-PUGT10) revealed a reduced ultimate load-carrying capacity compared to the reference NC-PUG specimen. The impact performance of the composite U-shaped NC-PUG specimen have remarkably improved due to the influence of PU grouting materials overlaid, with a higher ductility index, which tends to change the brittle nature of concrete to a ductile state. The energy absorption capacity at the failure stage of the NC-PUGT5 is 16 times that of the reference U-shaped NC-PUG specimen. The NC-PUGTB5 and NC-PUGT10 revealed impact strength, which is 52.28 times and 68.1 times more than the reference specimens, respectively. Reliability analysis indicated that about 80% of the U-shaped NC-PUG specimen can withstand 63 impact times at the failure stage. The finding showed that retrofitting concrete structures with PUG material is a promising and sustainable method to protect the concrete structure against dynamic load

Epidemiological features of lumpy skin disease outbreaks amongst herds of cattle in Bokkos, north-central Nigeria

Lumpy Skin Disease (LSD) is a severe viral transboundary disease of mostly cattle caused by LSD Virus (LSDV). This epidemiological survey of LSD amongst herds of cattle in Bokkos Local Government Area (LGA) of North Central Nigeria was carried out in 2019 as a response to farmers’ reports of repeated outbreaks of LSD in their herds of cattle. A focused group discussion with cattle farmers purposefully selected was used for the disease investigation and data collection. Twelve skin scab samples were collected from suspected cases within the study area. The viral attachment protein gene of the LSDV was amplified using polymerase chain reaction (PCR). Analysis of the focus group discussion revealed that all farmers interviewed practiced extensive farm management system and claimed that their animals shared same communal water points and grazing area. Furthermore, 47% (7/15) of the farmers have experienced LSD twice in their herds, while 27% (4/15) have had the outbreak thrice on their farms. The morbidity rates of LSD were 3% – 49% and mortality rates were 1% – 6%. Sixty percent of farmers claimed that incidence of LSD is related to season of the year. All farmers sell off their sick animals in the livestock market and confirmed LSD affects market price of their animals. PCR results revealed that in 91.6% (11/12) samples analysed, LSDV was detected. This study confirms LSD outbreaks based on PCR result and clinical signs and symptoms in Butura, Daffo and Kunduk of Bokkos LGA, North Central Nigeria