https://ajce.aut.ac.ir/ AUT Journal of Civil Engineering en daily 1 Thu, 17 Sep 2026 00:00:00 +0330 Thu, 17 Sep 2026 00:00:00 +0330 https://ajce.aut.ac.ir/article_6206.html Although moment-resisting frames exhibit satisfactory ductile performance, they suffer from limitations such as low stiffness. Consequently, designers attempt to enhance stiffness by reducing the span length of these frames. Meanwhile, design codes impose a minimum limit on the span-to-depth ratio for moment-resisting frames. This provision ensures the adequate formation of flexural plastic hinges at both ends of the beam. In this paper, a novel energy-absorbing steel system is proposed through numerical studies. In this system, instead of the conventional method of locally reducing beam cross-sections at both ends, a non-prismatic reduction of the beam cross-section along its entire length is introduced. As a result, energy dissipation occurs over a larger portion of the beam, thereby overcoming the aforementioned code limitation. After validation, two conventional lateral load-resisting systems and one system based on the idea presented in this study are considered, and their performance is evaluated using the finite element method. the proposed moment-resisting frame in this study exhibits approximately 2.15 times higher stiffness, twice the strength, and twice the energy absorption capacity compared to a conventional moment-resisting frame with a span-to-depth ratio of 7, while maintaining nearly equivalent plastic strain (PEEQ). Furthermore, when compared to a conventional moment-resisting frame with a span-to-depth ratio of 4 (which violates code provisions), the proposed system demonstrates comparable stiffness, strength, and energy dissipation, yet its PEEQ is about 37% lower, underscoring the superiority of the proposed concept. https://ajce.aut.ac.ir/article_6219.html This case study presents the successful application of the formal observational method for constructing eighteen 2000 m³ steel storage tanks on soft, problematic soil in a port in southwestern Iran. The site investigation revealed soft clayey soils with an allowable bearing capacity of only 36 kN/m², far less than the 170 kN/m² imposed by the proposed structures. To avoid costly deep foundations, a geotechnical solution was implemented using engineered soil filling (2 m thick multi‑layered fill of sand, gravel, and silty gravel) placed and compacted in the tank area. Finite Element Modeling (FEM) with the Hardening Soil Model predicted a long‑term consolidation settlement of 209 mm. A four‑month-controlled hydro‑test was conducted to preload the ground and accelerate primary consolidation, during which tanks were interconnected with flexible polyethylene pipes to accommodate differential movements. Settlement was meticulously monitored throughout and after the hydro‑test, with predefined alert, action, and alarm trigger levels as required by the observational method. Field results showed acceptable agreement with FEM predictions (average final settlement 234 mm). Long‑term average creep after hydro‑test was estimated at 30–50 mm over fifty years, well within the tolerance of flexible connections. The study concludes that the formal observational method, combined with soil replacement, is a viable and cost‑effective strategy for such challenging ground conditions. Key practical recommendations include the use of flexible hoses for all pipe connections and sliding restraints for access ladders. https://ajce.aut.ac.ir/article_6101.html Tuned mass dampers (TMDs) are widely used to mitigate seismic vibrations. However, studies have shown that TMD effectiveness diminishes in nonlinear systems and varies under different ground motions. This study investigates TMD performance in elastic and inelastic single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structures. Numerical models were constructed in OpenSees and subjected to 20 ground motion records. A parametric analysis examined the influence of TMD stiffness on displacement and inter-story drift using nonlinear time history analysis. The TMD stiffness ratio was systematically varied to identify an optimal value that maximizes response mitigation in nonlinear systems. Results indicate that traditional TMDs, tuned to the structure's fundamental frequency, often underperform in nonlinear systems. In some cases, they may amplify responses at higher ductility levels. For SDOF systems, reducing TMD stiffness below traditional values noticeably improves performance. Optimal TMD stiffness was determined by minimizing displacement ratios. For MDOF structures, reducing TMD stiffness below the traditional value decreases the response ratio in the upper stories of elastic structures and in all stories of inelastic structures. Notably, optimal TMDs selected by minimum total story displacement ratios differed from those selected by minimum inter-story drift ratios. This improved stiffness-based tuning strategy was subsequently applied to a Tuned Structural Mass Damper (TSMD) for nonlinear systems. The TSMD utilizes a portion of existing roof mass, thereby eliminating the need for additional weight. For SDOF systems, the TSMD demonstrated considerably better performance. In MDOF systems, TSMDs reduced inter-story drifts in inelastic structures and mitigated drift amplification in upper floors. https://ajce.aut.ac.ir/article_6135.html The use of non-destructive tests for predicting the durability of stabilized earthen blocks, in addition to environmental benefits, enhances the reliability, safety, and cost-effectiveness of structures. The durability of earthen blocks, defined as their ability to withstand environmental factors such as moisture, temperature variations, and erosion, plays a critical role in the lifespan of structures, reducing maintenance costs, and preserving the environment. This study aims to predict the durability properties of earthen blocks stabilized with synthetic copolymers and to develop predictive models based on a combination of destructive and non-destructive tests. The destructive tests included freeze–thaw cycles, wet–dry cycles, and erosion tests, while the non-destructive tests comprised water absorption, ultrasonic testing, contact angle measurement, and image processing. The results were modeled using Response Surface Methodology (RSM) in Design Expert software. The findings demonstrated that non-destructive tests effectively reflect the changes in blocks under various conditions, and their appropriate combination enables accurate prediction of earthen block performance. Multivariate models described the degradation trends, environmental cycle sensitivity, and the effects of polymer content and construction quality better than univariate models. Additionally, some non-destructive tests played a key role in durability prediction and can be selected as primary indicators for rapid assessment.The results indicate that combining non-destructive testing and modeling is an effective alternative to destructive testing, contributing to significant reductions in cost, time, and resources in sustainable earthen block projects. https://ajce.aut.ac.ir/article_6143.html The goal of this study is to determine how the setting of operational parameters in a laboratory-scale ball mill affects the fractal dimension and power draw. The trials were designed using Design Expert software. Three optimization tests were used to evaluate the resultant mathematical model, and the results indicated that it had adequate validity. Percent solids, grinding time, and mill speed are operational parameters of the mill examined in this research. The results show that increasing the percent solids reduces both the fractal dimension and the amount of particles smaller than 25 microns. Increasing the solid content from 25 to 45 percent and from 45 to 65 percent results in increases and decreases in power draw, respectively. As the grinding time increased, the power draw reduced. On the other hand, the reduction ratio and weight of particles smaller than 25 microns and, consequently, the fractal dimension increased as grinding time increased. The findings suggest that a higher mill speed will lead to a greater power draw. The fractal dimension, reduction ratio, and weight of particles smaller than 25 microns will all rise if the mill speed is increased from 55% to 75% of the critical speed. Increasing mill speed from 75% to 95% only increases power draw without significantly affecting other parameters. https://ajce.aut.ac.ir/article_6149.html The specialized instruments installed in earth dams play a crucial and vital role in monitoring, controlling, and managing the structural stability of these dams against applied forces and destructive natural factors. In this study, the Shiadeh earth dam, Currently, all precision instruments installed on the dam are out of order, rendering it impossible to record any potential deformations or settlements. Therefore, this research focuses on predicting the dam’s settlement during its operational phase, using historical data from the period when the instruments were fully functional and reliable. The prediction process was carried out using the advanced Radial Basis Function (RBF) algorithm. Within the scope of this study, both static and dynamic analyses were performed using the advanced PLAXIS 3D software to model the dam’s behavior and evaluate its settlement along the crest. The settlement prediction results obtained through the RBF algorithm, when compared with ground survey data from micro-geodetic points located on the downstream slope of the dam, show acceptable agreement, confirming the validity and accuracy of the modeling approach employed. The analysis of crest settlement using PLAXIS 3D modeling indicates that throughout various reservoir impoundment conditions across different seasons, the dam maintained a high safety factor and demonstrated stable performance. The maximum recorded deformation at the dam crest was found to be 0.789 meters. In the three-dimensional PLAXIS model, the effect of the strike-slip fault was also taken into account, which led to intensified deformation. https://ajce.aut.ac.ir/article_6164.html Climate change is one of the major environmental challenges of the present era. Among the key climatic parameters, atmospheric humidity plays a crucial role in defining weather conditions, agriculture, human health, and various economic activities. Therefore, This study explores the forecasting of relative humidity (RH) as a significant factor in hydro-climatology using the ARIMA(0,1,1) model. The research utilizes historical RH data from 1993 to 2022, coupled with the Mann-Kendall test for trend analysis and time series techniques, to evaluate both past and future RH trends. The results indicate that while RH experienced a decreasing trend over the past three decades, this trend was not statistically significant because of Z statistical equal= -0.01 that is fewer than 1.96 threshold. Further analysis using the ARIMA model forecasts a slight increase in RH in the coming years. These findings suggest that, despite the absence of significant past trends, RH may show modest upward changes, which could influence future hydro-climatic conditions. The study highlights the importance of incorporating RH forecasting into hydro-climatological models for more accurate predictions of climate behavior and water resource management. Additionally, the research underscores the utility of time series analysis in understanding long-term climate variables and their implications for environmental planning. https://ajce.aut.ac.ir/article_6197.html The application of concrete-filled double-steel tube (CFDST) joints in structural engineering has received great attention due to their high load-bearing capacity, excellent ductility, and favorable seismic performance. In this study, the load-bearing capacity of CFDST brace-column K-joints under monotonic loading was examined using the finite element method via Abaqus. The numerical results were successfully validated against experimental and analytical models from the literature, showing good agreement in load-displacement response and failure modes. A parametric study was conducted to investigate the effects of brace diameter, column-brace angle, and sandwiched concrete between the inner and outer steel tubes on the failure behavior and overall capacity of the K-joint. Results indicate that the load-bearing capacity decreases with increasing brace diameter and with increasing brace-column angle. Models with sandwiched concrete exhibited significantly higher peak loads compared to hollow joints, due to the composite action enhancing stiffness and delaying local buckling. The maximum load resisted by the CFDST K-joint reached 2755 kN, representing a 156% increase over the joint without concrete. Although concrete infill substantially improves stiffness and capacity, the compressive strength level of the concrete plays a negligible role; thus, even low-strength concrete can be effectively used. Comparison of ultimate displacements also revealed that concrete-filled models possess much greater ductility than hollow ones. The novelty of this study lies in the simultaneous parametric investigation of brace diameter, brace-to-column angle, and concrete infill on the load-bearing capacity of CFDST K-joints using a validated finite element model. https://ajce.aut.ac.ir/article_6222.html Existing non-power-generating dams represent substantial untapped renewable energy potential across developing regions, yet retrofitting decisions remain challenging owing to the need to balance economic viability against hydrological uncertainty while preserving primary dam objectives. This study presents a hybrid framework integrating Long Short-Term Memory (LSTM) neural networks with Stochastic Dynamic Programming (SDP) to optimise hydropower retrofitting under climate uncertainty. The LSTM forecasts monthly inflows from hydro-climatic predictors, while SDP derives an optimal retrofit/no-retrofit policy through empirically learned, non-stationary transition matrices, rather than the stationary transitions conventional approaches impose from historical frequencies. Test was performed on 37 years (1986–2023) of CHIRPS-derived monthly inflow data for Doma Dam, Nigeria, benchmarked against ARIMA, Classical Markov Chain, naïve rules, and perfect foresight bound. The hybrid model produced a mean NPV of $16.15M (99.6% success), statistically indistinguishable from Classical Markov, ARIMA, and Perfect Foresight. Its practical advantage lies in downside-risk management rather than mean-NPV outperformance, with higher CVaR₉₅ and lower coefficient of variation than Markov. Electricity tariff dominates NPV variance, followed by discount rate and capacity factor. The optimal policy proved robust to forecast perturbations up to ±20%. A Pettitt test located 1989 as the point of maximum divergence in inflows, though not significant; the LSTM still captured transition dynamics distinct from historical frequencies. Forecasting transferability, tested on another basin (Ojirami Dam), showed a substantial single-split accuracy drop, though a much smaller drop under walk-forward cross-validation. Overall, results support the retrofit's economic viability and the LSTM's forecasting superiority over ARIMA, without establishing decisive economic superiority. https://ajce.aut.ac.ir/article_6223.html In this study, an integrated machine learning (ML) and digital twin system was established to monitor and predict the stability of the slopes in real-time, based on the 847 monitoring samples that were taken daily from an instrumented loose deposit slope in Southwest China for about 28 months from January 2022 to May 2024. The SVM model performed best with the highest R² value among all the models, followed by the RF model and the XGBoost model, whereas the LSTM model did not perform well in the regression task with R² = −0.3675 and predictions were almost constant without capturing the temporal variations in FoS. The best overall performance (F1 = 0.955, accuracy = 96.09%) for stability classification on chronological test subset was obtained by XGBoost; SVM had a 100% recall on the marginally stable minority class, but a lower overall accuracy (85.94%). The shallow and deep pore-water pressure were the most important variables for the feature importance analysis (impurity/gain-based) and for the SHAP analysis, with 71-75% of the predictive importance. A digital twin platform has been created that provides integration of real-time sensor data, physics-based FEM complementation, quantile-based prediction intervals, and three-dimensional visualization, and can complete a full sensor-to-alert cycle in less than 30 seconds. It observed that conventional machine learning models (SVM, RF, XGBoost) can be used for prediction of the FoS for operational slope monitoring and the LSTM architecture used is not suitable for regression or safety-critical classification on this dataset.