A primary task to a project manager is to ensure that the project proceeds timely and on the budget. Earned value management (EVM) is the most common method to evaluate and further predict a project in terms of time and cost. Because of poor accuracy in forecasting different durations, the application of the EVM for schedule performance prediction has been limited. Therefore, in this paper, the Kalman filter was used to analyze the data and compare the results with those of a few hybrid machine learning (HML) techniques. The innovation of this study comes in the face of the prediction of the project time and cost by efficient machine learning algorithms based on some directly measurable variables. For this purpose, 398 data points from five different dam projects were used to predict two output variables by hybrid schemes of multilayer perceptron (MLP) algorithm combined with the genetic algorithm (GA) and particle swarm optimization (PSO), herein referred to as MLP-GA and MLP-PSO, respectively. Four input variables, namely earned schedule (month), earned value ($), actual progress (%), and actual cost (%), were considered for time prediction, and the time (month) was considered as an input variable for cost prediction. The results showed that early warnings for the risk of delay in project schedule and cost were generated in months 1 and 8 by the MLP-PSO, months 1 and 11 by the MLP-GA, and rather months 7 and NV (no value) for the Kalman filter, indicating the fast operation coupled with high accuracy of the HML algorithms, as compared to the Kalman filter. In addition, the two HML algorithms were compared based on statistical error indices to determine the best one. After reviewing the results, the MLP-PSO was found to outperform the MLP-GA in terms of convergence rate and accuracy. To the best of our knowledge, none of the previous studies have used the proposed hybrid algorithms (MELM-PSO and MELM-GA) for forecasting, simultaneously, the time and cost as two critical parameters in the project management, indicating the novelty of our research.

The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller’s superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure’s characteristics change.

Background: Consolidation of clays could be a very time-consuming procedure. However, preloading can increase the speed of consolidation; applying an electric field can accelerate this procedure significantly. The efficiency of the electroosmosis approach has been studied for different types of clays previously. At the same time, there is no information about the performance of the EO method for Tabriz gray clay. Methods: This paper investigates the effects of the EO technology on Tabriz gray clay using a modified oedometer consolidation apparatus. The achieved results of the EO consolidation of the Gray clay of Tabriz are compared with the standard Kaolin clay (KC-which produced industrially). Three different electrodes (Iron, Copper, and Aluminum) are employed to study about effects of the electrode type, and two different voltages (12 V and 24 V) are applied to assess the impact of the input electric field on the settlement of samples. Results: It is shown that the copper electrode leads to maximum volumetric strains while the iron electrode results in minimum volumetric strains. In addition, it is shown that the larger input voltage leads to larger settlements. The results indicate that the EO approach is more efficient for the Gray clay of Tabriz in comparison to the KC, where gray clay shows larger settlements while subjected to an electric field. Unconfined compression tests are also applied to some samples, and the EO method’s influence on the samples’ strength is discussed. Conclusions: The achieved results demonstrated that the EO method can be efficiently used to improve the consolidation and strength properties of the gray clay of Tabriz.

This paper presents a novel adaptive neurochaotic fuzzy control system based on type‐2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers under near‐field and far‐field earthquakes. In this proposed control system, the whole parameters of the plant are assumed to be completely unknown, the structural model is estimated using a multilayer perceptron neural network, and the system’s Jacobian is extracted. The online estimation model is used, and the controller parameters are adaptively trained using the extended Kalman filter and error back‐propagation method. Subsequently, the control force is applied to the active tuned mass damper, and the control objectives are met. The adaptive controller does not require initial settings, and a fractional‐order proportional‐integral‐derivative controller is added to maximize stability and robustness against seismic vibration. A simple adaptive controller optimized by a particle swarm is also presented as an innovation. Comparing the performance of the improved simple adaptive controller and adaptive neurochaotic fuzzy controller, the proposed controllers appear more efficient and accurate. However, the superiority of the novel adaptive neurochaotic fuzzy over the improved simple adaptive controller in reducing maximum displacement, acceleration, drift, and base shear while maintaining acceptable performance under parametric uncertainties and seismic conditions is substantial.

In this study we compared dam monitoring results with those of numerical analysis to propose a plan for the first reservoir impounding of the Iran-Madani Rock fill dam, ten years after the completion of its construction. The stability of the dam body has been assessed using numerical analysis and data obtained from sensors installed in the dam. The correctness and accuracy of the geotechnical parameters of the dam body materials were confirmed by comparing the results of numerical analysis and monitoring through back analysis. The linear correlation coefficients between the experimental data and the numerical results for settlement, pore water pressure, and total stress are 84%, 67%, and 99%, respectively. In addition, the agreement between the design assumptions with both the numerical analysis results and instrumentation data was examined. The arching ratio values obtained from instrumentation and numerical analysis in the core of the dam are 0.47 and 0.35, respectively, indicating the safety of the dam. Finally, a numerical sensitivity analysis was conducted to present a special impounding program for the dam, with a focus on controlling simultaneous changes in pore water pressure and effective stress in the clay core, ten years after the completion of the dam body construction.