Agent-based modeling and simulation of earthmoving operations
Bibliographic record
Abstract
Simulation has been used in construction modeling for decades, especially in large scale operations, such as earth moving, where heavy and costly equipment is used. Simulation can be used as a planning tool to analyze the time and cost of earthmoving operations. Current methods used in simulating earthmoving operations are based on Discrete-Event Simulation (DES), with recent efforts to introduce System Dynamics (SD) in a hybrid DES-SD approach. However, due to the predetermined nature of Discrete-Event Simulation (DES) models, some inflexibility is experienced when modeling earthmoving operations, which translates into a higher degree of difficulty in regards to model creation and a reduced accuracy of outputs. Although the introduction of System Dynamics (SD) contributed significantly to accounting for qualitative factors and strategic aspects of earthmoving operations, there still exists a need for enhancing the accuracy of capturing the logistics of these operations in a smart and flexible manner. \nWith the advancement of computational capabilities, Agent-Based Modeling and Simulation (ABMS) is rapidly replacing the conventional simulation techniques. This thesis introduces Agent-Based Modeling and Simulation (ABMS) as an effective tool for modeling earthmoving operations. First of all, it provides a generic methodology introduced for creating Agent-Based models for construction operations, based on a set of rules and criteria. Then, an Agent-Based (AB) model for earthmoving operations consisting of bulldozers, loaders, haulers and spotters is developed. The model in question governs the process logistics, information sharing, equipment properties as well as activity durations. Finally, a Java-Based software application (ABSEMO) is developed as an implementation of the proposed Agent-Based (AB) simulation model. Overall, the desired outcome is to create a smart system that has a flexible logic in addition to a good representation of model operations. \nA real-life case study of a riverbed excavation in a dam construction project is simulated using ABSEMO and the results are compared with those obtained from Discrete-Event Simulation (DES) models for verification. A percentage difference of 0.42% from the DES results was finally obtained, indicating that the model’s logic and flow of resources are indeed accurate. The proposed Agent-Based (AB) methodology and the developed model aim at enhancing current practices of modeling earthmoving operations by looking at these operations from an individual Agent-Based (AB) prospective. This allows the capturing of realistic behaviors, through crafting agents’ attributes, roles and interactions. The proposed methodology can be extended to general applications in construction management, where heterogeneity can be accounted for through replicating the different participants of construction projects in Agent-Based (AB) models as well as studying the emergent behavior of their interactions on the system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".