Papers by Abdulazeez Rotimi
Glass Structures & Engineering, Dec 20, 2023
Water Balance and Streamflow Modelling Using the Soil and Water Assessment Tool (SWAT): A Case of Gaojiaping Watershed in South China
A substantial reduction in the amount of carbon dioxide emissions resulting from human activities... more A substantial reduction in the amount of carbon dioxide emissions resulting from human activities is required to curb global warming and this has led to the development of numerous measures to ensure that cleaner and more efficient energy sources are utilised in all facets of people's daily lives. Globally, buildings account for almost half of the energy use in both developed and developing nations. Commercial buildings account for a sizeable proportion of this building energy consumption and this trend will probably continue to increase. Therefore, concerted efforts are currently being directed at the development and application of effective building strategies and measures to improve energy efficiency in buildings. This study evaluates the impact of various energy efficiency measures and technologies on the thermal and energy performance of UK hotel buildings using a whole building dynamic simulation software (application to Hilton hotels) with a focus on the knock-on effects that these technologies will have on the overall energy performance and efficiency of UK hotels, either installed individually or in various combinations. The study employs a quantitative research approach underpinned by the thermal analysis simulation of various case study hotel buildings to address the supposition that dynamic climatic conditions, building energy consumption estimates, building energy efficiency improvement strategies and building thermal behaviour can be appropriately simulated to enhance the energy efficiency of commercial buildings and abate the unfavourable effects of global climate change. The outcome of the research presents a practical approach of estimating the energy consumption of operational hotel buildings with relative accuracy aimed at testing the suitability of various 7.4.
Research Square (Research Square), May 17, 2023
The most crucial mechanical property of concrete is compression strength (CS). Insufficient compr... more The most crucial mechanical property of concrete is compression strength (CS). Insufficient compressive strength can therefore result in severe failure and is very difficult to fix. Therefore, predicting concrete strength accurately and early is a key challenge for researchers and concrete designers. High-Strength Concrete (HSC) is an extremely complicated material, making it challenging to simulate its behaviour. The CS of HSC was predicted in this research using an Adaptive Neuro-fuzzy Inference system (ANFIS), Backpropagation neural networks (BPNN), Gaussian Process Regression (GPR), and NARX neural network (NARX) In the initial case, whereas in the second case, an ensemble model of k-Nearest Neighbor (k-NN) was proposed due to the poor performance of model combination M1 & M2 in ANFIS, BPNN, NARX and M1 in GPR. The output variable is the 28-day CS (MP) and the input variables are cement (Ce) Kg/m 3 , water (W) Kg/m 3 , superplasticizer (S) Kg/m 3 , coarse aggregate (CA) Kg/m 3 , and Fine aggregate (FA) Kg/m 3. The outcomes depict that the suggested approach is predictively consistent for forecasting the CS of HSC, to sum up. The MATLAB 2019a toolkit was employed to generate the MLs learning models (ANFIS, BPNN, GPR, and NARX), whereas E-Views 11.0 was used for pre-and post-processing of the data, respectively. The model for BPNN and NARX modelling was trained and validated using MATLAB code. The outcome depicts that, the Combination M3 partakes the preeminent performance evaluation criterion when associated to the other models, where ANFIS-M3 prediction outperforms all other models with NSE, R 2 , R = 1, and MAPE = 0.261 & 0.006 in both the calibration and verification phases, correspondingly, in the first case, In contrast, the ensemble of BPNN and GPR surpasses all other models in the second scenario, with NSE, R2, R = 1, and MAPE = 0.000, in both calibration and verification phases Comparisons of 3 total performance showed that the proposed models can be a valuable tool for predicting the CS of 47 HSC.
Compatibility of Hybrid Neuro-Fuzzy Model to Predict Reference Evapotranspiration in Distinct Climate Stations
The aim of this study is to model Reference Evapotranspiration (ET0) in Nigeria and Cyprus with M... more The aim of this study is to model Reference Evapotranspiration (ET0) in Nigeria and Cyprus with Maiduguri and Larnaca as a case study region. Adaptive Neuro Fuzzy Inference System (ANFIS) which utilized 3 membership function owing to its fine mapping capability was employed for the modeling purpose. Multiple Linear Regression (MLR) model was also developed. The results were compared to Penman-Monteith (FAO-56-PM) model. Monthly average of long-term climate data including minimum temperature, maximum temperature, relative humidity, and wind speed were used as inputs to the models. The performance of the models was evaluated by two global statistics of Root Mean Square Error (RMSE), and Determination Coefficient (DC). The results indicated that ANFIS had better performance than MLR models. The results also showed ANFIS was capable of modeling ET0 in the study regions efficiently, but had better performance in Maiduguri than in Larnaca region.
Multi-state comparison of machine learning techniques in modelling reference evapotranspiration: A case study of Northeastern Nigeria
Monthly reference evapotranspiration (ET0) was predicted for Bauchi and Maiduguri stations locate... more Monthly reference evapotranspiration (ET0) was predicted for Bauchi and Maiduguri stations located in the northeastern semiarid region of Nigeria. The data for 34 years (1983-2016) were used including maximum and minimum temperature, relative humidity, and wind speed. The models were developed using artificial neural networks (ANN), support vector regression and multiple linear regression (MLR). The most influential weather parameters and the best computing technique were also investigated. FAO Penman-Monteith (FAO-56-PM) is regarded as the sole method for estimating ET0, it is therefore employed in this study as the benchmark ET0. Two statistical indicators of root mean square error (RMSE) and determination coefficient (R2) were used to assess the performance of the models. The results showed that relative humidity has better performance in single input models but inclusion of wind speed can produce best performance for the 3 inputs models. However, the study revealed that ANN had the better ET0 prediction capability in both stations, and 3 inputs model with minimum temperature, relative humidity, and wind speed led to superior efficiency. The general results demonstrated that the ANN, SVR and MLR can be employed for reliable estimation of ET0 in the study stations.
Feasibility of artificial intelligence and CROPWAT models in the estimation of uncertain combined variable using nonlinear sensitivity analysis
Reference evapotranspiration (ET0) estimation with reliable accuracy is critical for the manageme... more Reference evapotranspiration (ET0) estimation with reliable accuracy is critical for the management of water resources and irrigation practices. The aim of this study is to estimate ET0 using CROPWAT model in Kano and Katsina meteorological stations of northwestern Nigeria. Artificial neural network (ANN) and multiple linear regression (MLR) were also developed for comparison. Monthly mean data for 34 years (1983–2016) including maximum, minimum and mean temperatures (Tmax, Tmin and Tmean), relative humidity (RH) and wind speed (U2) were used as inputs. Penman Monteith (FAO-56-PM) regarded as the standard method for computing ET0 was used as the benchmark. Initially, nonlinear correlation analysis was carried out to determine the best input variable. Thereafter, 7 models were developed based on different combinations to ascertain the most reliable for comparison to Crop Water and Irrigation Requirements Program of FAO (CROPWAT) model. The normalized Determination coefficient (R2) and root mean square error (RMSE) were used as the criteria for checking the performance of the models. The results showed that RH was the most dominant input, model 6 that has a combination of Tmax, RH and U2 provided the most reliable performance. The results also demonstrated that CROPWAT model is comparable in performance to ANN and MLR and can be efficiently used to estimate ET0 in the study stations with R2 of 0.923, 0.962 and RMSE of 0.065, 0.046 in the validation phase for Kano and Katsina stations, respectively.
High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm
Asian Journal of Civil Engineering
Advances in concrete construction, Apr 25, 2017
The phenomenon of concrete column shortening has been widely acknowledged since it first became a... more The phenomenon of concrete column shortening has been widely acknowledged since it first became apparent in the 1960s. Axial column shortening is due to the combined effect of elastic and inelastic deformations, shrinkage and creep. This study aims to investigate the effects of ambient temperature, relative humidity, cement hardening speed and aggregate type on concrete column shortening. The investigation was conducted using a column shortening prediction model which is underpinned by the Eurocode 2. Critical analysis and evaluation of the results showed that the concrete aggregate types used in the concrete have significant impact on column shortening. Generally, aggregates with higher moduli of elasticity hold the best results in terms of shortening. Cement type used is another significant factor, as using slow hardening cement gives better results compared to rapid hardening cement. This study also showed that environmental factors, namely, ambient temperature and relative humidity have less impact on column shortening.
High-strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm
The most crucial mechanical property of concrete is compression strength (CS). Insufficient compr... more The most crucial mechanical property of concrete is compression strength (CS). Insufficient compressive strength can therefore result in severe failure and is very difficult to fix. Therefore, predicting concrete strength accurately and early is a key challenge for researchers and concrete designers. High-Strength Concrete (HSC) is an extremely complicated material, making it challenging to simulate its behaviour. The CS of HSC was predicted in this research using an Adaptive Neuro-fuzzy Inference system (ANFIS), Backpropagation neural networks (BPNN), Gaussian Process Regression (GPR), and NARX neural network (NARX) In the initial case, whereas in the second case, an ensemble model of k-Nearest Neighbor (k-NN) was proposed due to the poor performance of model combination M1 & M2 in ANFIS, BPNN, NARX and M1 in GPR. The output variable is the 28-day CS (MP) and the input variables are cement (Ce) Kg/m3, water (W) Kg/m3, superplasticizer (S) Kg/m3, coarse aggregate (CA) Kg/m3, and Fin...
Implementation of Nonlinear Computing Models and Classical Regression for Predicting Compressive Strength of High-Performance Concrete
Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques
GSC Advanced Engineering and Technology, Oct 30, 2022
Feasibility of computational intelligent techniques for the estimation of spring constant at joint of structural glass plates: a dome-shaped glass panel structure
Glass Structures & Engineering
Compressive strengths of concrete by partially replacing sand with iron-ore waste
GSC Advanced Engineering and Technology, 2022
Sand is extracted at a rate more than its restoration. Nevertheless, the availability of sand in ... more Sand is extracted at a rate more than its restoration. Nevertheless, the availability of sand in the growing demand of the construction industry remains a challenge due to cost and quality problems. This study investigates the compressive strength of concrete by partially replacing sand with iron-ore waste. Experimental investigations were conducted to study the compressive strength, physical, mechanical and fresh property of concrete containing iron-ore waste. During the experiment, concrete cubes were prepared with 10-100% composition of iron ore waste to evaluate their compressive strength. Results from the experimentation revealed that, concrete cubes prepared by partially replacing sand with iron ore waste often yield a better compressive strength than a conventional concrete. More so, the densities of concrete cubes were observed to remain consistent but increased slightly, notably at 10% and 20% of waste replacement. Meanwhile, at 30% waste replacement, there was reduction in...
Reuse of waste plastic as an additive in asphalt concrete: An overview
GSC Advanced Engineering and Technology
This review is centered on the use of waste plastics as an additive for road construction. This r... more This review is centered on the use of waste plastics as an additive for road construction. This review discusses extensively the state-of-the-art methods that can be employed in achieving a waste plastic bituminous mixture. These approaches may help by contributing to the reduction of plastic wastes which constitute nuisance to the environment. Plastics are currently at the top of the international waste management agenda - a global problem, but with regional variations. Several studies have shown that plastic can stay on earth for thousands of years without deterioration and poses a great threat to the atmosphere and humanity when disposed improperly. Hence, there is the need to find a solution to such menace. The utilization of waste plastic to enhance service properties in road paving applications was considered a long time ago. Nowadays, it has become a real alternative and several literature reviews in many countries have confirmed the significance and beneficial effects of uti...
Compressive strengths of concrete by partially replacing sand with iron-ore waste
GSC Advanced Engineering and Technology
Sand is extracted at a rate more than its restoration. Nevertheless, the availability of sand in ... more Sand is extracted at a rate more than its restoration. Nevertheless, the availability of sand in the growing demand of the construction industry remains a challenge due to cost and quality problems. This study investigates the compressive strength of concrete by partially replacing sand with iron-ore waste. Experimental investigations were conducted to study the compressive strength, physical, mechanical and fresh property of concrete containing iron-ore waste. During the experiment, concrete cubes were prepared with 10-100% composition of iron ore waste to evaluate their compressive strength. Results from the experimentation revealed that, concrete cubes prepared by partially replacing sand with iron ore waste often yield a better compressive strength than a conventional concrete. More so, the densities of concrete cubes were observed to remain consistent but increased slightly, notably at 10% and 20% of waste replacement. Meanwhile, at 30% waste replacement, there was reduction in...
Feasibility of artificial intelligence and CROPWAT models in the estimation of uncertain combined variable using nonlinear sensitivity analysis
2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), 2021
Reference evapotranspiration (ET0) estimation with reliable accuracy is critical for the manageme... more Reference evapotranspiration (ET0) estimation with reliable accuracy is critical for the management of water resources and irrigation practices. The aim of this study is to estimate ET0 using CROPWAT model in Kano and Katsina meteorological stations of northwestern Nigeria. Artificial neural network (ANN) and multiple linear regression (MLR) were also developed for comparison. Monthly mean data for 34 years (1983–2016) including maximum, minimum and mean temperatures (Tmax, Tmin and Tmean), relative humidity (RH) and wind speed (U2) were used as inputs. Penman Monteith (FAO-56-PM) regarded as the standard method for computing ET0 was used as the benchmark. Initially, nonlinear correlation analysis was carried out to determine the best input variable. Thereafter, 7 models were developed based on different combinations to ascertain the most reliable for comparison to Crop Water and Irrigation Requirements Program of FAO (CROPWAT) model. The normalized Determination coefficient (R2) and root mean square error (RMSE) were used as the criteria for checking the performance of the models. The results showed that RH was the most dominant input, model 6 that has a combination of Tmax, RH and U2 provided the most reliable performance. The results also demonstrated that CROPWAT model is comparable in performance to ANN and MLR and can be efficiently used to estimate ET0 in the study stations with R2 of 0.923, 0.962 and RMSE of 0.065, 0.046 in the validation phase for Kano and Katsina stations, respectively.
Multi-state comparison of machine learning techniques in modelling reference evapotranspiration: A case study of Northeastern Nigeria
2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), 2021
Monthly reference evapotranspiration (ET0) was predicted for Bauchi and Maiduguri stations locate... more Monthly reference evapotranspiration (ET0) was predicted for Bauchi and Maiduguri stations located in the northeastern semiarid region of Nigeria. The data for 34 years (1983-2016) were used including maximum and minimum temperature, relative humidity, and wind speed. The models were developed using artificial neural networks (ANN), support vector regression and multiple linear regression (MLR). The most influential weather parameters and the best computing technique were also investigated. FAO Penman-Monteith (FAO-56-PM) is regarded as the sole method for estimating ET0, it is therefore employed in this study as the benchmark ET0. Two statistical indicators of root mean square error (RMSE) and determination coefficient (R2) were used to assess the performance of the models. The results showed that relative humidity has better performance in single input models but inclusion of wind speed can produce best performance for the 3 inputs models. However, the study revealed that ANN had the better ET0 prediction capability in both stations, and 3 inputs model with minimum temperature, relative humidity, and wind speed led to superior efficiency. The general results demonstrated that the ANN, SVR and MLR can be employed for reliable estimation of ET0 in the study stations.
Compatibility of Hybrid Neuro-Fuzzy Model to Predict Reference Evapotranspiration in Distinct Climate Stations
2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), 2021
The aim of this study is to model Reference Evapotranspiration (ET0) in Nigeria and Cyprus with M... more The aim of this study is to model Reference Evapotranspiration (ET0) in Nigeria and Cyprus with Maiduguri and Larnaca as a case study region. Adaptive Neuro Fuzzy Inference System (ANFIS) which utilized 3 membership function owing to its fine mapping capability was employed for the modeling purpose. Multiple Linear Regression (MLR) model was also developed. The results were compared to Penman-Monteith (FAO-56-PM) model. Monthly average of long-term climate data including minimum temperature, maximum temperature, relative humidity, and wind speed were used as inputs to the models. The performance of the models was evaluated by two global statistics of Root Mean Square Error (RMSE), and Determination Coefficient (DC). The results indicated that ANFIS had better performance than MLR models. The results also showed ANFIS was capable of modeling ET0 in the study regions efficiently, but had better performance in Maiduguri than in Larnaca region.
Feasibility of artificial intelligence and CROPWAT models in the estimation of uncertain combined variable using nonlinear sensitivity analysis
2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)
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Papers by Abdulazeez Rotimi