Amirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X571020251222Development of Gauss-Legendaire-Hermite-3Point (GLH-3P) Formulation for Linear and Nonlinear Analysis of Earthquake-Affected StructuresDevelopment of Gauss-Legendaire-Hermite-3Point (GLH-3P) Formulation for Linear and Nonlinear Analysis of Earthquake-Affected Structures17251748598310.22060/ceej.2026.23247.8136FAMehdiBabaeiDepartment of Civil Engineering, University of Bonab, Bonab, Iran0000-0002-9080-1893Mohammad RezaHanafiDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran0009-0003-2238-2740AlirezaRahaeiDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran0000-0002-9101-0794Journal Article20240603The dynamic behavior of structures under seismic loading is a critical consideration in civil engineering, requiring accurate and efficient analytical methods. Nonlinear time history analysis methods, which involve solving the structure's motion equations over time, serve as valuable tools in this regard. These methods consist of two key components: an acceleration model for each step and a numerical integration technique for tackling nonlinear equations. This research presents an effective formulation for nonlinear dynamic analysis of structures, referred to as the Gauss-Legendre-Hermit-3P Method. It's based on the implicit three-point Gauss integration rule and employs third-order Hermite interpolation for approximating intermediate steps. The proposed formula can analyze nonlinear geometric and material systems and handle various loading patterns. To evaluate the performance of this formulation, a series of linear and nonlinear systems were subjected to the El-Centro earthquake record and analyzed. The results obtained from the analyses of the new method were compared with those of other commonly used methods, including the semi-analytical Duhamel integral method, the pseudo-analytical Newmark-beta method, and the Wilson-theta method. The proposed formulation exhibits a significant advantage over other methods in terms of accuracy, stability, convergence, and computational cost. It can be seamlessly implemented into finite element software and employed for nonlinear time history analysis of single-degree-of-freedom and multi-degree-of-freedom structures.The dynamic behavior of structures under seismic loading is a critical consideration in civil engineering, requiring accurate and efficient analytical methods. Nonlinear time history analysis methods, which involve solving the structure's motion equations over time, serve as valuable tools in this regard. These methods consist of two key components: an acceleration model for each step and a numerical integration technique for tackling nonlinear equations. This research presents an effective formulation for nonlinear dynamic analysis of structures, referred to as the Gauss-Legendre-Hermit-3P Method. It's based on the implicit three-point Gauss integration rule and employs third-order Hermite interpolation for approximating intermediate steps. The proposed formula can analyze nonlinear geometric and material systems and handle various loading patterns. To evaluate the performance of this formulation, a series of linear and nonlinear systems were subjected to the El-Centro earthquake record and analyzed. The results obtained from the analyses of the new method were compared with those of other commonly used methods, including the semi-analytical Duhamel integral method, the pseudo-analytical Newmark-beta method, and the Wilson-theta method. The proposed formulation exhibits a significant advantage over other methods in terms of accuracy, stability, convergence, and computational cost. It can be seamlessly implemented into finite element software and employed for nonlinear time history analysis of single-degree-of-freedom and multi-degree-of-freedom structures.https://ceej.aut.ac.ir/article_5983_0cb82dbdcda47e2ad7b7aaf69573906e.pdfAmirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X571020251222A Sustainable Approach to Recycling Multi-Layer Aseptic PackagingA Sustainable Approach to Recycling Multi-Layer Aseptic Packaging17491768598510.22060/ceej.2026.23617.8189FAPouyaShoaieFaculty of Environment, University of Tehran, Tehran, Iran0009-0000-9931-9916AlirezaBazarganFaculty of Environment, University of Tehran, Tehran, IranJournal Article20241020Recycling aseptic cartons poses significant challenges due to their complex multi-layer structure. This study focuses on optimizing the dissolution of low-density polyethylene (LDPE) to develop an efficient recycling method for these materials. Cartons from various brands were collected, cleaned, and dried, with LDPE dissolved using a solvent blend of xylene, toluene, and gasoline. The Plackett-Burman experimental design was employed to identify key factors affecting dissolution. The optimal solvent ratio was determined to be 50:25:25 (v/v) gasoline, xylene, and toluene. Critical parameters, in order of influence, included the solid-to-liquid ratio, double-layer seams, temperature, and time. Under optimal conditions (120°C for 30 minutes), complete LDPE dissolution was achieved across all brands. Recovery rates reached 100% for LDPE and 90% for the solvent. Traditional methods for separating aluminum from paper proved ineffective, but an eddy current separator (ECS) was identified as a viable solution. Furthermore, omitting the hydropulping step enhanced LDPE dissolution. This research paves the way for more effective recycling strategies, supporting sustainable waste management and advancing the circular economy. Additional studies on scalability, economic feasibility, and environmental impact are required for industrial application.Recycling aseptic cartons poses significant challenges due to their complex multi-layer structure. This study focuses on optimizing the dissolution of low-density polyethylene (LDPE) to develop an efficient recycling method for these materials. Cartons from various brands were collected, cleaned, and dried, with LDPE dissolved using a solvent blend of xylene, toluene, and gasoline. The Plackett-Burman experimental design was employed to identify key factors affecting dissolution. The optimal solvent ratio was determined to be 50:25:25 (v/v) gasoline, xylene, and toluene. Critical parameters, in order of influence, included the solid-to-liquid ratio, double-layer seams, temperature, and time. Under optimal conditions (120°C for 30 minutes), complete LDPE dissolution was achieved across all brands. Recovery rates reached 100% for LDPE and 90% for the solvent. Traditional methods for separating aluminum from paper proved ineffective, but an eddy current separator (ECS) was identified as a viable solution. Furthermore, omitting the hydropulping step enhanced LDPE dissolution. This research paves the way for more effective recycling strategies, supporting sustainable waste management and advancing the circular economy. Additional studies on scalability, economic feasibility, and environmental impact are required for industrial application.https://ceej.aut.ac.ir/article_5985_fccc64972a9468a11f125cadb090e89e.pdfAmirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X571020251222Simultaneous Determination of the Location and Time of IPCC System Installation and Relocation in Open Pit Mines Considering the Time Value of MoneySimultaneous Determination of the Location and Time of IPCC System Installation and Relocation in Open Pit Mines Considering the Time Value of Money17691788598610.22060/ceej.2026.24259.8282FAFardinShirmohammadiDepartment of Mining Engineering, Amirkabir University of Technology, Tehran, IranSajjadAfraeiDepartment of Mining Engineering, Amirkabir University of Technology, Tehran, Iran0000-0002-8476-6358MajidAtaee PourDepartment of Mining Engineering, Amirkabir University of Technology, Tehran, Iran0000-0002-2387-8831Journal Article20250610As the depth of the mines increases, the loading rate of each truck per unit of time decreases, so that the number of trucks may increase in order to compensate for a drop in production. In contrast, the continuous transportation system, is capable of carrying more volumes of minerals and wastes due to its higher carrying capacity and functionality on steep slopes. For this reason, this topic has attracted the attention of researchers. This research examines the optimal level of the in-pit crusher station, so that its using time and location are optimized based on the time value of money. To this end, a modified target function is used to consider the capital and operational costs of both truck and conveyor transport systems according to the time value of the money and apply the impact of capital costs on the initial locations and subsequent locations of the crusher station. The results show that the use of the year when the method of changing the method from the Shovel-truck system to the IPCC system leads to the lowest net present value of the costs, and the use of location information that year to determine the optimal level has achieved the best results. According to the findings in the hypothetical mine, the fifth year in the fifth year was the most optimized place and time for the use of the IPCC system, and the cost of using the system was 5.16% in the first year of the mine.As the depth of the mines increases, the loading rate of each truck per unit of time decreases, so that the number of trucks may increase in order to compensate for a drop in production. In contrast, the continuous transportation system, is capable of carrying more volumes of minerals and wastes due to its higher carrying capacity and functionality on steep slopes. For this reason, this topic has attracted the attention of researchers. This research examines the optimal level of the in-pit crusher station, so that its using time and location are optimized based on the time value of money. To this end, a modified target function is used to consider the capital and operational costs of both truck and conveyor transport systems according to the time value of the money and apply the impact of capital costs on the initial locations and subsequent locations of the crusher station. The results show that the use of the year when the method of changing the method from the Shovel-truck system to the IPCC system leads to the lowest net present value of the costs, and the use of location information that year to determine the optimal level has achieved the best results. According to the findings in the hypothetical mine, the fifth year in the fifth year was the most optimized place and time for the use of the IPCC system, and the cost of using the system was 5.16% in the first year of the mine.https://ceej.aut.ac.ir/article_5986_fd45c64e026040dbcb83395829d2aea5.pdfAmirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X571020251222Optimization of Electrocoagulation for Reducing the Organic Load of Landfill Leachate: A Case Study of the Tehran Kahrizak LandfillOptimization of Electrocoagulation for Reducing the Organic Load of Landfill Leachate: A Case Study of the Tehran Kahrizak Landfill17891806598710.22060/ceej.2026.24982.8369FAHaniyehMehrshadFaculty of Engineering, Kharazmi University, Tehran, IranMohammadDelnavazCivil engineering department, Faculty of Engineering, Civil Engineering Department, Kharazmi University0000-0002-6843-2649Journal Article20251025The leachate generated at municipal solid-waste landfills contains complex, recalcitrant, and potentially toxic constituents; therefore, it should be treated before discharge to the environment and groundwater. In this study, leachate samples were collected from the Aradkouh Waste Management Complex (Kahrizak, Tehran, Iran) and treated using an electrocoagulation (EC) process. The effects of initial pH (4, 7, and 9), current density (25.31, 37.97, and 50.63 mA/cm²), reaction time (15–60 min), and inter-electrode distance (1, 2, and 4 cm) were evaluated for the removal of chemical oxygen demand (COD), total dissolved solids (TDS), and total suspended solids (TSS). Experiments were conducted in a plexiglass batch reactor equipped with three aluminum electrodes and powered by a direct-current supply. Under the optimum conditions (pH 9, 50.63 mA/cm², 60 min, and 2 cm), removal efficiencies of COD, TDS, and TSS reached 37.8%, 34.3%, and 40.2%, respectively. In addition, concentrations of Cr, Pb, Zn, and Fe were determined in raw and treated leachate using atomic absorption spectrometry, with corresponding removal efficiencies of 43.75%, 41.43%, 37.50%, and 27.23%. Overall, the results indicate that electrocoagulation can serve as an effective pretreatment option for reducing the pollutant load of highly concentrated landfill leachate.The leachate generated at municipal solid-waste landfills contains complex, recalcitrant, and potentially toxic constituents; therefore, it should be treated before discharge to the environment and groundwater. In this study, leachate samples were collected from the Aradkouh Waste Management Complex (Kahrizak, Tehran, Iran) and treated using an electrocoagulation (EC) process. The effects of initial pH (4, 7, and 9), current density (25.31, 37.97, and 50.63 mA/cm²), reaction time (15–60 min), and inter-electrode distance (1, 2, and 4 cm) were evaluated for the removal of chemical oxygen demand (COD), total dissolved solids (TDS), and total suspended solids (TSS). Experiments were conducted in a plexiglass batch reactor equipped with three aluminum electrodes and powered by a direct-current supply. Under the optimum conditions (pH 9, 50.63 mA/cm², 60 min, and 2 cm), removal efficiencies of COD, TDS, and TSS reached 37.8%, 34.3%, and 40.2%, respectively. In addition, concentrations of Cr, Pb, Zn, and Fe were determined in raw and treated leachate using atomic absorption spectrometry, with corresponding removal efficiencies of 43.75%, 41.43%, 37.50%, and 27.23%. Overall, the results indicate that electrocoagulation can serve as an effective pretreatment option for reducing the pollutant load of highly concentrated landfill leachate.https://ceej.aut.ac.ir/article_5987_7f9d88fe83d3e7fce3136e510b0a9a38.pdfAmirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X571020251222Analysis of Mathematical Models for Predicting the Mechanical Resistance of Concrete Reinforced with Steel Fibers Using Experimental and Machine Learning MethodsAnalysis of Mathematical Models for Predicting the Mechanical Resistance of Concrete Reinforced with Steel Fibers Using Experimental and Machine Learning Methods18071838599010.22060/ceej.2026.20086.8164FAMohmmad HosseinTaghavi ParsaUniversity of Qom0000-0002-2968-7294MortezaEsmaeiliIran University of Science and TechnologyMohammadrezaAdlparvarAssociate professor civil engineering department technical& engineering faculty university of qomJournal Article20240816The purpose of this article is to present an optimal mathematical model for predicting the mechanical resistance of concrete reinforced with steel fibers. For this purpose, while studying and investigating the relationships of previous researchers for predicting the compressive, tensile and bending strengths of fiber concrete reinforced with steel fibers, the optimal mathematical relationships governing the problem have been investigated by machine learning method. The focus of the current research is on those concretes reinforced with steel fibers, which are made of smooth, wavy and double-crossed fibers on a macro scale. During this research, the mathematical relationships extracted from the machine learning method, which is used to model the prediction of compressive, tensile and bending strengths of concrete reinforced with steel fibers and by applying the symbolic regression method based on a database consisting of 2283 The provided international data is checked. The efficiency of the models used in this research has been measured using error analysis statistics such as RMSE and MAPE. The results show that the parameters of the size of the largest aggregate, modulus of elasticity, compressive strength of control concrete, water-cement ratio, volume percentage and length of fibers, dimensional ratio of fibers and tensile or bending strength related to the type of output investigated for fiber concrete have the greatest effect. They are resistant to prediction. Examining the results shows that the formulas presented for predicting the strength of reinforced concrete for the examined fibers have considerable accuracy compared to the previous mathematical relationships.The purpose of this article is to present an optimal mathematical model for predicting the mechanical resistance of concrete reinforced with steel fibers. For this purpose, while studying and investigating the relationships of previous researchers for predicting the compressive, tensile and bending strengths of fiber concrete reinforced with steel fibers, the optimal mathematical relationships governing the problem have been investigated by machine learning method. The focus of the current research is on those concretes reinforced with steel fibers, which are made of smooth, wavy and double-crossed fibers on a macro scale. During this research, the mathematical relationships extracted from the machine learning method, which is used to model the prediction of compressive, tensile and bending strengths of concrete reinforced with steel fibers and by applying the symbolic regression method based on a database consisting of 2283 The provided international data is checked. The efficiency of the models used in this research has been measured using error analysis statistics such as RMSE and MAPE. The results show that the parameters of the size of the largest aggregate, modulus of elasticity, compressive strength of control concrete, water-cement ratio, volume percentage and length of fibers, dimensional ratio of fibers and tensile or bending strength related to the type of output investigated for fiber concrete have the greatest effect. They are resistant to prediction. Examining the results shows that the formulas presented for predicting the strength of reinforced concrete for the examined fibers have considerable accuracy compared to the previous mathematical relationships.https://ceej.aut.ac.ir/article_5990_3cba81c5c6cac4ce77157631fc2dc277.pdfAmirkabir University of TechnologyAmirkabir Journal of Civil Engineering2588-297X571020251222A Review of the Applications of Machine Learning in Asphalt Pavement EngineeringA Review of the Applications of Machine Learning in Asphalt Pavement Engineering18391872598410.22060/ceej.2026.23492.8175FAM.MDadaeiDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, IranM.mEntezariDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, IranRashidTanzadehDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran0000-0002-7918-6172FereidoonMoghadas NejadDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran0000-0003-3830-4555Journal Article20240904Incorporating deficient design and construction methods in the pavement industry, exacerbated by unoptimized maintenance plans, has led to unprecedented economic and social costs. Therefore, novel technologies and up-to-date science are urgently needed. Artificial Intelligence (AI), one such technology, is used to develop machines and algorithms that mimic the human brain. AI has proven cost- and time-effective in enhancing asphalt pavement design, material production, construction, and maintenance management, compared to traditional solutions. This article delves into the applications of Machine Learning (ML), a subset of AI, in pavement engineering by reviewing 150 related scientific articles. The results show ML has been employed in seven research areas: design optimization (11% of studies), asphalt performance prediction (8%), prediction of asphalt mixture characteristics (33%), detection of surface defects (19%), classification of surface defects (2%), prediction of pavement functional indices (21%), and maintenance plans optimization (6%). Statistical analyses on publication frequency, algorithms used, and input features for predicting performance were presented to outline trends, research gaps, and achievements. It is concluded that ML is an indispensable tool for improving, optimizing, and conducting critical processes in pavement design, material production, construction, and management. Consequently, further research into ML applications in pavement engineering is necessary. This will facilitate the development of cutting-edge technologies like Digital Twins (DTs) for the industry.Incorporating deficient design and construction methods in the pavement industry, exacerbated by unoptimized maintenance plans, has led to unprecedented economic and social costs. Therefore, novel technologies and up-to-date science are urgently needed. Artificial Intelligence (AI), one such technology, is used to develop machines and algorithms that mimic the human brain. AI has proven cost- and time-effective in enhancing asphalt pavement design, material production, construction, and maintenance management, compared to traditional solutions. This article delves into the applications of Machine Learning (ML), a subset of AI, in pavement engineering by reviewing 150 related scientific articles. The results show ML has been employed in seven research areas: design optimization (11% of studies), asphalt performance prediction (8%), prediction of asphalt mixture characteristics (33%), detection of surface defects (19%), classification of surface defects (2%), prediction of pavement functional indices (21%), and maintenance plans optimization (6%). Statistical analyses on publication frequency, algorithms used, and input features for predicting performance were presented to outline trends, research gaps, and achievements. It is concluded that ML is an indispensable tool for improving, optimizing, and conducting critical processes in pavement design, material production, construction, and management. Consequently, further research into ML applications in pavement engineering is necessary. This will facilitate the development of cutting-edge technologies like Digital Twins (DTs) for the industry.https://ceej.aut.ac.ir/article_5984_7f2cba89a7116c7c6b0a769572d5fad9.pdf