Ziziphus spina christi for Sustainable Agroforestry Farming in Arid Land of Khartoum State of Sudan

Mustafa Abdalla Nasre Aldin,Hussein Alawad Seid Ahmed,Mohamed El Mukhtar Ballal,Adil Mahgoub Farah 강원대학교 산림과학연구소 2023 Journal of Forest Science Vol.39 No.1

Cow pea (Vigna unguiculata) was intercropped with Ziziphus spina-christi as summer forage in two consecutive seasons of 2017 and 2018. The aims to find out suitable agroforestry practice for saline soils of Khartoum State. And to investigate effect of tree spacing on forage biomass yield under semi -irrigated systems. Completely randomized block design with 3 replicates was conducted for this trial. Thus Z.spina-christi that fixed at 4×4 m was intercropped with cowpea at 1 m and 1.5 m spacing from trees trunk. Tree growth parameters were measured in terms of tree height, tree collar diameter, tree crown diameter and fruit yield per tree. While crop were parameters were determined in terms of plant height, number of plant, forage biomass yield per ha and land equivalent ratio. Soil profile of 1×1 m and 1.5 m depth was excavated and its features were described beside its chemical and physical properties were analyzed for 0-10 cm, 0-30 cm, and 30-60 cm and 60-100 cm layers. The results revealed that soil pH, CaCO3, SAR, ESP, and EC ds/m were increased by increasing soil depths. Meanwhile tree growth in terms of tree height was significant in the first season 2017 when compared with tree collar diameter and tree crown diameter. Also significant differences were recorded for tree growth when compared with sole trees in the second season in 2018. Tree fruit showed marked variations between the two seasons, but it was higher under intercropping particularly at ZS2. Crop plant height was highly significant under sole cropping than intercropping in first season in 2017. In contrast forage biomass yield was significant under intercropping in ZS1 and ZS2 treatments. Land equivalent ratio was advantageous under this agroforestry system particularly under ZS2. Thus it recorded 5 and 9 for ZS2 in the two consecutive seasons respectively. Therefore, it is feasible to introduce this agroforestry system under such arid lands to provide summer forage yield of highly nutritive value and low cost for animals feed as well as to increase farmers’ income and to halt desertification and to sequester carbon.

Machine learning-based techniques to facilitate the production of stone nano powder-reinforced manufactured-sand concrete

Zanyu Huang,Qiuyue Han,Adil Hussein Mohammed,Arsalan Mahmoodzadeh,Nejib Ghazouani,Shtwai Alsubai,Abed Alanazi,Abdullah Alqahtani Techno-Press 2023 Advances in nano research Vol.15 No.6

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

Predicting the splitting tensile strength of manufactured-sand concrete containing stone nano-powder through advanced machine learning techniques

Manish Kewalramani,Hanan Samadi,Adil Hussein Mohammed,Arsalan Mahmoodzadeh,Ibrahim Albaijan,Hawkar Hashim Ibrahim,Saleh Alsulamy Techno-Press 2024 Advances in nano research Vol.16 No.4

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

Machine learning techniques for reinforced concrete’s tensile strength assessment under different wetting and drying cycles

Ibrahim AlBaijan,Danial Fakhri,Adil Hussein Mohammed,Arsalan Mahmoodzadeh,Hawkar Hashim Ibrahim,Khaled Mohamed Elhadi,Shima Rashidi 국제구조공학회 2023 Steel and Composite Structures, An International J Vol.49 No.3

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete’s tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work’s significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

The gene expression programming method to generate an equation to estimate fracture toughness of reinforced concrete

Ahmadreza Khodayari,Danial Fakhri,Adil Hussein Mohammed,Ibrahim AlBaijan,Arsalan Mahmoodzadeh,Hawkar Hashim Ibrahim,Ahmed Babeker Elhag,Shima Rashidi 국제구조공학회 2023 Steel and Composite Structures, An International J Vol.48 No.2

Complex and intricate preparation techniques, the imperative for utmost precision and sensitivity in instrumentation, premature sample failure, and fragile specimens collectively contribute to the arduous task of measuring the fracture toughness of concrete in the laboratory. The objective of this research is to introduce and refine an equation based on the gene expression programming (GEP) method to calculate the fracture toughness of reinforced concrete, thereby minimizing the need for costly and time-consuming laboratory experiments. To accomplish this, various types of reinforced concrete, each incorporating distinct ratios of fibers and additives, were subjected to diverse loading angles relative to the initial crack (α) in order to ascertain the effective fracture toughness (Keff) of 660 samples utilizing the central straight notched Brazilian disc (CSNBD) test. Within the datasets, six pivotal input factors influencing the Keff of concrete, namely sample type (ST), diameter (D), thickness (t), length (L), force (F), and α, were taken into account. The ST and α parameters represent crucial inputs in the model presented in this study, marking the first instance that their influence has been examined via the CSNBD test. Of the 660 datasets, 460 were utilized for training purposes, while 100 each were allotted for testing and validation of the model. The GEP model was finetuned based on the training datasets, and its efficacy was evaluated using the separate test and validation datasets. In subsequent stages, the GEP model was optimized, yielding the most robust models. Ultimately, an equation was derived by averaging the most exemplary models, providing a means to predict the Keff parameter. This averaged equation exhibited exceptional proficiency in predicting the Keff of concrete. The significance of this work lies in the possibility of obtaining the Keff parameter without investing copious amounts of time and resources into the CSNBD test, simply by inputting the relevant parameters into the equation derived for diverse samples of reinforced concrete subject to varied loading angles.