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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.

This paper presents a model for equipment selection in earthmoving operations, utilizing multi‐attribute utility theory, analytical hierarchy process and computer simulation. Fleet configurations in the developed model are generated randomly from predefined fleet scenarios within a specified range. Simulation experiments are conducted for these generated configurations. The performance of these configurations is obtained from simulation experiments in the form of four measures which represent loader utilization, hauler utilization, project duration and project total cost. The utility values which represent the degree of satisfaction with those measures are estimated. These utility values are multiplied by their corresponding measures’ weights, calculated utilizing the analytical hierarchy process, in order to estimate the expected utility for each configured fleet. The fleet configuration that has the largest utility value is selected as the optimum fleet for the case at hand. A numerical example is presented to illustrate the different features of the developed model.

The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model's performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA).

An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks.

Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors.

Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.

The dispatching of trucks in earthmoving and like operations is worthy of examination because of potential emission reductions and savings through the appropriate allocation of trucks to excavators and dump sites. The paper aims to discuss this issue.

Truck dispatching is performed through linear programming (LP) and the effect of truck allocation on unit emissions and unit costs established. Number of trucks, unit cost and unit emissions are all considered as objective functions. A cut and fill operation on a road project provides a numerical case study.

It is demonstrated analytically that the minimum unit emissions solution is the same as that for minimum unit cost. Numerical results from the case study, including sensitivity analyses on the underlying parameters, support this conclusion.

The LP dispatching solution, based on minimizing truck numbers and unit costs, accordingly impacts the environment the least in terms of emissions. The paper's results will be of interest to those designing and managing earthmoving and like operations for production, cost and emissions.

While LP has been used by others to examine optimum unit cost dispatching, this paper is original in examining the dispatching or truck allocation based on both unit cost and unit emissions, and showing the relationship between the optima for both.

Motivated by the high cost of material movements in road construction projects, past studies have used analytical methods to optimize materials logistics plans. A key shortcoming of these methods is their inability to capture the uncertain, dynamic and complex characteristics of the road construction material logistics. Failure to incorporate these characteristics can lead to sub-optimal results. The purpose of this study is to propose the use of discrete event simulation (DES) to address the existing shortfall.

Despite the powerful capabilities of DES models in capturing the operational complexities of construction projects, they have not been previously utilized to optimize the material logistics of road construction projects. The proposed DES-based method in this research captures the operational details of material logistics and uses a heuristic approach to overcome the combinatorial problem of numerous choices. The method was applied to a 63.5 km real-world road construction project case to demonstrate its capabilities.

Six different material types from 28 material sources were used in the case. Approximately 1.5% of the material logistics costs were saved by following the proposed method and choosing appropriate material sources.

This research contributes to the body of knowledge by leveraging the capabilities of DES and presenting a novel method for improving the materials logistics plan of road construction projects. The proposed method provides practitioners with the basis for capturing the key operational details that were overlooked in the past. The proposed method can be adopted in road construction projects to reduce the overall material procurement cost.

This research aims to investigate the adoption of future technologies in earthmoving applications. The increased development in automated driving systems (ADS) has opened up significant opportunities to revolutionize mobility and to set the path for technologies, such as electrification. The research also aims to explore the impact of automation on electromobility in earthmoving applications.

This paper adopts a multi-objective simulation-based optimization approach using machine learning in earthmoving applications.

This study concludes that ADS is “conditionally” an enabler for electrification. The study highlights and explains how local and global factors affect this conclusion. In addition to that, the research explores the impact of the equipment size on the integration of future mobility technologies. The shift from “elephant to ants” in the fleet selection resulted in improved feasibility from the integration of ADS in electrification.

This research provides fundamental considerations in the assessment of the impact of autonomous driving solutions on electromobility in the construction industry.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

The performance of earthmoving operations, in terms of emissions, production and cost, is dependent on many variables and has been the study of a number of publications. Such publications look at typical operation design and management, without establishing what the penalties or bonuses might be for non-standard, but still observed, practices. To fill this gap in knowledge, this paper examines alternative loading policies of zero waiting-time loading, fractional loading and double-sided loading, and compares the performance of these with standard single-sided loading.

Original recursive relationships, that are amenable to Monte Carlo simulation, are derived. Case study data are used to illustrate the emissions, production and cost penalties or bonuses.

Double-sided loading contributes the least impact to the environment and is the most cost effective. Zero waiting-time loading performs the worst in terms of environmental impact and cost. Minimizing truck waiting times through using fractional loading is generally not an attractive policy because it leads to an increase in unit emissions and unit costs. The consequences of adopting fractional loading are detailed. Optimum unit emissions and optimum unit cost are coincident with respect to fleet size for single- and double-sided loading policies. That is, by minimizing unit cost, as in traditional practice, then least impact on the environment is obtained. Not minimizing unit cost will lead to unnecessary emissions.

The results of this paper will be of interest to those designing and managing earthmoving operations.

All modeling and results presented in the paper do not exist elsewhere in the literature.

The development of communication and artificial intelligence technologies has raised interest in connectivity and increased autonomy of automated earthmoving equipment for earthwork. These changes are motivating work to reduce uncertainties, in terms of improving equipment object detection capability and reducing strikes and accidents on site. The purpose of this study is to illustrate industrial drivers for automated earthwork systems; identify the specific capabilities which make the transformation happen; and finally determine use cases that create value for the system. These three objectives act as components of a technology roadmap for automated and connected earthwork and can guide development of new products and services.

This paper used a text mining approach in which the required data was captured through a structured literature review, and then expert knowledge was used for verification of the results.

Automated and connected earthwork can enhance construction site and its embraced infrastructure, resilience by avoiding human faults during operations. Automating the monitoring process can lead to reliable anticipation of problems and facilitate real-time responses to unexpected situation via connectedness capabilities. Research findings are presented in three sections: industrial perspectives, trends and drivers for automated and connected earthwork; capabilities which are met by technologies; and use cases to demonstrate different capabilities.

This study combines the results of disintegrated and fragmented research in the area of automated and connected earthwork and categorises them under new capability levels. The identified capabilities are classified in three main categories including reliable environmental perception, single equipment decision-making toward safe outcomes and fleet-level safety enhancement. Finally, four different levels of automation are proposed for earthwork technology roadmap.