Scopus Research — Riyadh Abdulabbas Ali Alsultani

Accurate and timely prediction of ozone concentrations is critical for meteorological monitoring and developing effective environmental policies, promoting both resilience and sustainability, as prolonged exposure to elevated pollutant levels poses significant risks to human health and ecosystem integrity. In fact, ozone concentration monitoring and reduction align with global efforts to reduce its negative impact on air pollution (SDG 3: Good Health and Well-being) and enhance urban environmental resilience (SDG 11: Sustainable Cities and Communities). However, the complex physicochemical processes governing tropospheric ozone formation present substantial challenges for precise modeling. Recent advancements in data-driven machine-learning approaches have demonstrated considerable potential in addressing these challenges, particularly in predicting ozone concentrations. Deep learning models, in particular, have been employed to analyze ozone data as time series, leveraging historical concentration values collected over hours or days. Despite these advancements, opportunities remain for enhancing predictive accuracy through the application of state-of-the-art attention-based architectures, such as the Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Transformer model. This study investigates the efficacy of the self-attention mechanism in predicting ozone concentrations across three monitoring stations in Malaysia: Kuala Lumpur (KL), Putrajaya (PJ), and Klang, using hourly data for the duration between 2012 and 2019, which have been collected from the Department of Environment (DOE). Ministry of Natural Resources and Environmental Sustainability (NRES). The results reveal exceptional performance, with high coefficients of determination (R2) and low mean squared errors (MSE) achieved for PJ, KL, and Klang. Furthermore, the Transformer model significantly reduced both training and inference times (70 s and 1 s, respectively) compared to the LSTM model (110 s and 7.7 s), particularly when long-term historical ozone data is required for prediction. These findings highlight the robust capabilities of self-attention mechanisms in enhancing the accuracy and efficiency of ozone concentration forecasting, warranting further investigation and validation across diverse urban environments globally and contributing to sustainable urban planning and resilience of environmental monitoring systems. © 2025 The Authors

The paper deals with setting a 3D finite element interaction between soil and bridge piers modeled in DIANA software parallel processing. The main focus is laid on the sustainability of the structure, including unification of hydrodynamic pressure presented by currents-waves of water flow and the seismic effect with the nonlinearity of soil and concrete. Water forces are applied as a distributed loading to the pile foundation of the bridge based on two forms of hydrodynamic pressure, Morison and fifth-order Stokes theory. The paper presents an investigation of structural bridge pier stimulation under elastic conditions including influence of current-wave of flow. The velocity of flow, wave characteristics, and seismic intensity are discussed concerning the structural behavior of the piers which include relative moment, displacement, acceleration, shear, and hydrodynamic pressure. This work has demonstrated that pressure changes due to earthquakes in the hydrodynamic regime modify the behavior of the pier by developing added internal forces in the lower pier and high values of displacement and acceleration at the pier upper. The wave effect must be included in the resilient and sustainable design of bridge infrastructure. © 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data. © 2024 The Authors

The purpose of this study is to assess Haditha Dam’s catchment area and accessible surface area in order to guarantee that these regions can hold water without being at risk of floods. Using topographic data, the study simulated the two-dimensional catchment area and flow area below the dam. The monthly increase in water storage was then computed using the water balance equation and HEC RAS software. These increments were used to determine the required flow that might be utilized to run the dam more efficiently. Significant outflows were found at the start of the operational year. These volumes will probably cause water to accumulate, water levels to increase quickly, and heights to climb. In order to make sure that these regions can store water without running the danger of flooding, the goal of this study is to assess the catchment area of a contemporary dam and its accessible surface area. The study generated a two-dimensional catchment region and flow area below the dam using topography data. The water balance equation and HEC RAS software were then used to determine the monthly increase in water storage. The necessary flow that could be utilized to run the dam as effectively as possible was calculated using these increments. This assessment provides a comprehensive analysis of the dam’s capacity to manage water storage efficiently and mitigate flood risks, contributing to sustainable water management practices. © 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

This article reports the durability performance of modified concrete with silica nanoparticles and a high volume of waste ceramic tiles under varying elevated temperatures. Ordinary Portland cement (OPC) was replaced with 60% waste ceramic tiles powder (WTCPs) and supplemented with 2, 4, 6, 8, and 10% nanopowders from waste glass bottles (WGBNPs) as a rich source of silica. The natural aggregates (both coarse and fine) were fully replaced by the crushed waste ceramic tiles (WTCAs). After 28 days of curing, the modified specimens were exposed to varying elevated temperatures (200, 400, 600, and 800 °C) in a furnace followed by air cooling. Tests such as residual compressive strength, weight loss, ultrasonic plus velocity, visual appearance, and microstructural analysis were conducted. Additionally, analysis of variance (ANOVA) was used to validate the performance of the proposed predictive equations, as well as their terms, using p-values and F-values. It was discerned that OPC substitution with WTCPs and WGBNPs significantly improved the concrete’s performance under elevated temperatures. It is observed that the addition of 2, 4, 6, 8, and 10% WGBNPs lowered the concrete deterioration by increasing the residual strength and reducing both internal and external cracks. This study provides some new insights into the utilization of WTCPs and WGBNPs to produce sustainable and eco-friendly modified concrete with high spalling resistance characteristics at elevated temperatures. © 2024 by the authors.

Background: Climate change poses significant challenges to the durability of concrete bridge structures, particularly regarding the corrosion of reinforcement. Iraq, due to its geographical location, is particularly vulnerable to greenhouse gas emissions, with carbon dioxide (CO2) being the most prominent. The country's heavy reliance on energy resources like coal, gas, and oil exacerbates air pollution, further compounding environmental concerns. Corrosion of reinforcement in concrete infrastructure, including bridges, is primarily driven by the presence of atmospheric CO2. The risk of corrosion increases with rising CO2 levels associated with global warming, leading to potentially catastrophic damage that is costly to repair. Methods: This study employs a probabilistic technique to predict the damage potential of concrete infrastructure exposed to carbonation resulting from elevated temperatures and CO2 concentrations. Results: The findings reveal a significant increase in the risk of damage from carbonation in certain regions of Iraq, with potential rises exceeding 400% by 2100. Additionally, rising temperatures elevate the likelihood of chloride impact by up to 15% over the same period. However, these assessments do not consider changes in ocean acidity in marine exposure, which could further exacerbate the effects of climate change on concrete infrastructure. Conclusion: The results underscore the urgent need for proactive measures to mitigate the impact of rising atmospheric CO2 levels on the durability of bridge structures in the face of climate change. © 2024 The Author(s). Published by Bentham Open.

A laboratory study was conducted in the Hashimiya region in mid-Iraq, in which collapsible silty clay soil was subjected to control mechanical energy. Three random soil samples were spatially selected and carefully mixed for later testing. Each test was repeated three times and the results average was taken. The conditions included soil wetting up to 50% saturation (S) and dynamic power loads up to 300 kJ. The aim was to evaluate the efficiency of geotextile reinforcement in resisting soil collapse due to soil wetting. Tests were conducted using a test box and a specific amount of dynamic energy, various experiments were performed. The geotextile layers were placed within the soil column, in multiples of 10 cm apart. Remarkably, under conditions of dynamic energy of 200 kJ and S= 0.35, the soil collapse potential (Ie) was reduced to less than 5% with the implementation of geotextile layers spaced 10 cm apart. Subsidence reduction percentages (SR%) varied depending on the saturation levels and number of geotextile layers, with higher saturation levels and larger distances between layers leading to lower SR% and vice versa. It is found SR is 3.95, 19.78, and 40.58% in the case of 1, 2, and 3 layers of geotextile reinforcement, degree of saturation of S= 0.25, and 300 kJ dynamic energy, whereas, SR is 2.16, 14.79, and 30.44% in the case of 1, 2, and 3 layers of geotextile reinforcement, degree of saturation of S=0.5 and 300 kJ dynamic load. This research emphasizes the critical role of geotextile reinforcement in mitigating collapsible silty clay soil instability and provides insights into effective for enhancing soil stability in areas exposed to such geological challenges. © 2024, Union of Iraqi Geologists. All rights reserved.

The goal of the experiment described in this paper was to examine the effects of structure orientation (0°–90°) and fluid–structure interaction (FSI) under combined water loads, represented by water current and waves, and earthquake actions, on the dynamic response of a reduced-scale bridge pier specimen with pile foundation. The peak relative displacement and peak acceleration of the specimen are measured using the first time innovative in Iraq, Reality Water–Structure–Earthquake Interaction Test (RWSEIT). The findings are given and analyzed concerning water depths, current speed, wave characteristics, earthquake amplitudes, and structural orientations. A numerical model of the examined specimen with three dimensions (3D) was constructed, and the findings were successfully confirmed using the data from the experiments. A pile foundation bridge pier's 3D structural response under orientations that cannot be tested in a lab was computed using the constructed numerical model. The complicated dynamically produced FSI effects on the response of coastal pile foundation bridges may be better understood according to the research's experimental and numerical findings. © 2023, The Author(s).

An essential component of the project feasibility assessment is the conceptual cost estimate. In actuality, it is carried out based on the estimator's prior expertise. However, budgeting and cost control are planned and carried out ineffectively as a result of inaccurate cost estimates. The purpose of this article is to introduce an intelligent model to improve modeling approaches accuracy throughout early phases of a project's development in the construction sector. A support vector machine model, which is computationally effective, is created to calculate the conceptual costs of building projects. To get accurate estimates, the suggested neural network model is trained using a cross-validation method. Through the research of the literature and interviews with experts, the cost estimate's influencing elements are determined. As training instances, the cost information from 40 structures is used. Two potent intelligence methods-Nonlinear Regression (NR) and Evolutionary Fuzzy Neural Interface Model (EFNIM)- are offered to illustrate how well the suggested model performs. Based on the readily accessible dataset from the relevant literature in the construction business, their results are contrasted. The computational findings show that the intelligent model that is being provided outperforms the other two potent methods. During the planning and conceptual design phase, the inaccuracy is satisfied for a project's conceptual cost estimate. Case studies demonstrate how SVMs may help planners anticipate the cost of construction in an effective and precise manner © 2023, Mathematical Modelling of Engineering Problems.All Rights Reserved.