Search results

Buried pipelines under various soil embankment heights are cost-effective alternatives to transporting liquid products. This paper aims to assist pipeline architects and professionals in selecting the most cost-effective buried reinforced concrete pipelines under deep embankment soil with minor structural reinforcement while meeting shear stress requirements, safety and reliability constraints.

It is unfeasible to experimentally assess pipeline efficiency with high soil fill depth. Thus, to fill this gap, this research uses a dependable finite element analysis (FEA) to conduct a parametric study and carry out such an issue. This research considered reinforced concrete pipes with diameters of 25, 50, 75, 100, 125 and 150 cm at depths of 5, 10, 15 and 20 m.

According to this research, the proposed best pipeline diameter-to-thickness (D/T) proportions for soil embankment heights 5, 10, 15 and 20 m are 8.75, 4.8, 3.5 and 3.1, correspondingly. The cost-effective reinforced concrete (RC) pipeline thickness dramatically rises if the soil embankment reaches 20 m, indicating that the soil embankment depth highly influences it. Most of the analyzed reinforced concrete pipelines had a maximum deflection value of less than 1 cm, telling that the FEA accurately identified the pipeline width, needed flexural steel reinforcement, and concrete crack width while avoiding significant distortion.

The cost-effective thickness for the analyzed structured concrete pipes was calculated by considering the lowest required value of steel reinforcement. An algorithm was developed based on the parametric scientific findings to predict the ideal pipeline D/T ratio. A construction case study was also shown to assist architects and professionals in determining the best reinforced concrete pipeline geometry for a specific soil embankment height.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.