بحوث سكوبس — الاء حسين عبد الامير عبدان

This study investigates various machine learning models, namely multi-layer perceptron (MLP) and generalized regression neural network (GRNN), for predicting the mechanical properties of high compressive strength geopolymer mortars. Both classification (MLPC and GRNNC) and regression (MLPR and GRNNC) based models, with MLP architectures comprising 1 and 2 hidden layers, are developed. Furthermore, three optimization algorithms, namely Levenberg–Marquardt (LM), momentum (M), and resilient backpropagation (R), are utilized. The models’ inputs are alkali concentrations, heat-curing temperatures, and curing periods. The results showed that the classification-based MLP with one hidden layer and resilient optimizer (MLPC-1-R) outperformed the other models by recording lower prediction deviations and high prediction accuracy. On the other hand, the regression-based models showed promising results and less sensitivity to the optimization type, unlike the classification-based ones. Finally, the resilient backpropagation (R) optimizer tends to provide consistent and high performance for both classification and regression-based models. © 2025 The Author(s).

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/).

This study reveals the unprecedented potential of artificial intelligence (AI) models in accurately predicting the mechanical properties of microwave-cured geopolymer mortars, thereby addressing a critical gap in the integration of AI and materials science. Furthermore, applying advanced algorithmic structure and machine learning is an unexplored area in extant literature. Four parameters: conventional curing period (time- NH), conventional curing temperature (temp-NH), microwave power (W), and microwave curing period (time-MW) are considered to generate a comprehensive dataset to predict compressive strength (CS) and flexural strength (FS). Four AI models have been adopted and rigorously compared: deep learning neural network (DL-NN), probabilistic neural network (PNN), radial basis function neural network (RBF-NN), and support vector machine (SVM). The performance was evaluated using various statistical matrices and visualization graphs. The findings showed that the DL-NN model performs exceptionally well in predicting compressive and flexural strengths, with correlation coefficient (R) values of 0.966 and 0.931, mean absolute error (MAE) of 3.544 MPa and 0.990 MPa, and root mean square error (RMSE) of 5.856 MPa and 1.442 MPa, respectively. These results demonstrate the model's ability to handle the complex, non-linear relationships inherent in the data. Meanwhile, the PNN model ranked second, with R values of 0.930 and 0.833, MAE values of 5.151 MPa and 1.566 MPa, and RMSE values of 7.947 MPa and 2.089 MPa, respectively. Furthermore, the carbon dioxide (CO2) emissions and embodied energy were investigated. Finally, a sensitivity analysis was conducted to assess the relative importance of each parameter on the mechanical properties. © 2025 Elsevier Ltd

The growing concerns about global warming and its adverse impacts on the environment, particularly with Portland cement production, developed the need to search for sustainable alternatives in the context of the construction industry. This study presents a transformative approach by utilizing alkali-activated concrete (ACC) that leverages industrial by-products presented in cement kiln dust (CKD), fly ash (FA), and silica fume (SF) to produce an environmentally friendly binder while maintaining enhanced mechanical properties. Furthermore, this study integrates experimental investigation with advanced artificial intelligence (AI) models to predict the compressive strength of ACC. The experimental part investigates the utilization of different binder ratios (0–100%), activator concentrations (6–14 mol/L), activator to binder ratio (0.35–0.55), and the ratio of SiO2/NaOH (1.5–3.5) on the compressive strength of alkali-activated concrete (ACC). Furthermore, this study investigates the potential of various AI models, such as Multiple Linear Regression (MLR), Multi-Layer Perceptron (MLP), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) in predicting the compressive strength of ACC. In this regard, binder ratios, activator concentrations, activator to binder ratio, and the ratio of SiO2/NaOH were utilized as input, while compressive strength was utilized as output. Moreover, the performance of the adopted models was assessed utilizing various statistical matrices and graphical appraisals. The results from the experimental part reveal that utilizing 25% CKD, 50% FA, and 25% FA combined with 10–14 mol/L of activator concentrations, activator to binder ratio of 0.55, and SiO2/NaOH ratio up to 3.5 recorded a compressive strength exceeding 30 Mpa. Meanwhile, the results of the AI models reveal that the MLP tends to provide superior performance, recording minimal prediction deviation and high prediction accuracy (R2 = 0.9), followed by the PNN model. Finally, the sensitivity analysis results showed that FA and SF content, as well as the average weight, have the highest impact on compressive strength. © Springer Nature Switzerland AG 2025.

Three crucial stations, Kufa, Al-Abbasiya, and Al-Manathera, were used to monitor the water quality of the Euphrates River in Al-Najaf Governorate in 2023 and 2024. The following 14 critical components were assessed pH, dissolved oxygen (DO), nitrate (NO3-), calcium (Ca2+), magnesium (Mg2+), total hardness (TH), potassium (K+), sodium (Na+), sulfate (SO42-), chloride (Cl-), total dissolved solids (TDS), electrical conductivity (EC), alkalinity (Alk), and turbidity (turbidity). The mean, standard deviation, maximum, and minimum values for each element were computed for each year. According to the statistics, in 2023, it was discovered that Al-Abbasiya had the greatest values for pH and dissolved oxygen, while Al-Manathera had the highest values for most elements. Kufa station has the lowest dissolved oxygen readings in 2024. Values for the water quality index (WQI) climbed from 81.4-101.6 in 2023 to 150.8-190.2 in 2024 across all stations when the weighted arithmetic index technique was used. This caused the water quality index at the Kufa and Abbasiya stations to drop from "good" to "poor," but Al-Manathera station continued to be categorized as poor by international standards. In order to preserve the river system and guarantee the sustainability of water resources, these findings highlight the urgent necessity for management actions. ©2025 The authors.

This research examines the effect of cement dust pollution on soil properties in Iraq's arid western region, particularly in the context of the area surrounding the Kubaisa Cement Plant. Spatial modeling methods were used by researchers to collect and analyze 32 soil samples at two different distances from the plant to assess chemical and physical changes to soil properties. Major findings showed higher concentrations of heavy metals like lead and lithium, higher alkalinity levels of soil, and higher particulate matter and CO2 concentrations close to the source of the pollution. Even with present pollutants, ecological and environmental indices revealed low levels of contamination and ecological risk, on the whole. The findings indicate the continuing effects of industrial emissions on soil integrity and establish the necessity for focused measures to avert ecological and public health risks and help protect the environment. © 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/). This article is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/)

In this article the affiliation details for Saif Saad Mansoor, Haitham Abdulmohsin Afan and Jumaa Mohammed Ahmad were incorrectly given as ‘Upper Euphrates Center for Sustainable Development Research, Ramadi 31001, Iraq.’ but should have been ‘Upper Euphrates Center for Sustainable Development Research, University of Anbar, Ramadi, 31001, Iraq’. The original article has been corrected. © Springer Nature Switzerland AG 2025.

Various challenges associated with using construction materials for delivering sustainable land management and infrastructure have been addressed using nanotechnology in the extensive literature. This study explores the utility of artificial intelligence (AI) models in forecasting soil properties, including compressive strength and the crashing load of active soils stabilized by organosilane nanomaterials, which is considered an unexplored area. In this regard, three AI models (multilayer perceptron, radial basis function, and generalized neural network) have been adopted to simulate the soil properties. For the model development, six parameters known as plasticity index (PI), liquid limit (LL), natural moisture content (NMC), activity (A), clay content (C), and nanomaterial-to-water ratio (Mix per.) have been considered as inputs to the AI models. Based on various statistical matrices and graphical appraisals, the multilayer perceptron (MLP) model showed significantly high-performance predicting crash (q) load and compressive strength (UCS) compared to other models with obtained R2 values of 0.926 and 0.957, respectively. Meanwhile, both radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models demonstrate a significantly poor performance for both parameters, with an R2 values ranging from 0.803 to 0.837, indicating the lack of generalization ability and recognition of complicated relationships and patterns. © 2025 The authors.

To achieve industrial growth while maintaining environmental integrity, Sustainable Material Management (SMM) is crucial. As manufacturing systems rapidly advance, maximising efficiency and minimising waste have become increasingly important. This study explores the integration of SMM within modern manufacturing practices, highlighting its potential to reduce environmental impacts, conserve resources and foster innovation. The paper focuses on maximising material efficiency throughout the material lifecycle – from extraction to disposal – by utilising life-cycle assessments, circular economy principles and emerging technologies. Case studies and industry trends demonstrate the compelling benefits of SMM, which not only enhance economic performance and reduce environmental footprints but also advance technology. The results of this research can help researchers, policymakers and manufacturers implement sustainable manufacturing strategies that improve economic and industrial competitiveness while promoting sustainability. Consequently, this paper emphasises the critical need for collaborative, systemic approaches to sustainable manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Accurately predicting factor of safety (FoS) is a crucial step in the process of designing and stability assessment of embankments, particularly in those supported by geogrid encased stone columns (GESCs), by ensuring optimized design, preventing structural failure and reducing construction cost. In this regard, this study utilizes the potential of four machine learning models presented in probabilistic neural network (PNN), generalized regression neural network (GRNN), radial basis function neural network (RBF), and artificial neural network (ANN) in predicting the FoS of embankments supported by stone columns encased with geogrid. The adopted models were evaluated in the training, validation, and testing phases using various statistical matrices and graphical analysis. The findings showed that the GRNN model offers performance across all phases, particularly in the testing phase, with an R-value of 0.985, indicating a high linearity between the actual values and the predicted value induced, as well as lower prediction deviations. The ANN also obtains a reliable prediction by offering high prediction accuracy (R = 0.970) and lower prediction deviations, highlighting it as the second-best model and a strong alternative to the GRNN model. Conversely, the RBF model showed the lowest performance across all phases, showing higher prediction deviations and lower prediction accuracy. The analysis revealed that the key factors affecting safety are the diameter of the stone column, the embankment angle, and the stiffness of the geogrid. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

Climate change is one of the trending terms in the world nowadays due to its profound impact on human health and activity. Extreme drought events and desertification are some of the results of climate change. This study utilized the power of AI tools by using the long short-term memory (LSTM) model to predict the drought index for Anbar Province, Iraq. The data from the standardized precipitation evapotranspiration index (SPEI) for 118 years have been used for the current study. The proposed model employed seven different optimizers to enhance the prediction performance. Based on different performance indicators, the results show that the RMSprop and Adamax optimizers achieved the highest accuracy (90.93% and 90.61%, respectively). Additionally, the models forecasted the next 40 years of the SPEI for the study area, where all the models showed an upward trend in the SPEI. In contrast, the best models expected no increase in the severity of drought. This research highlights the vital role of machine learning models and remote sensing in drought forecasting and the significance of these applications by providing accurate climate data for better water resources management, especially in arid regions like that of Anbar province. © 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.