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This study proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases.

The development of EW-Onto started from defining the concepts and building taxonomies for earthwork operations and equipment following the METHONTOLOGY approach. In addition, several rules have been extracted from safety codes and implemented as SWRL rules. The ontology has been implemented using Protégé. The consistency of EW-Onto has been checked and it has been evaluated using a survey.

The assessment of EW-Onto by experts indicates an adequate level of consensus with the framework, as an initial step for explicit knowledge exchanges within the earthwork domain.

The use of an ontology within the earthwork domain can help: (1) link and identify the relationships between concepts, define earthwork semantics, and classify knowledge in a hierarchical way accepted by experts and end-users; (2) facilitate the management of earthwork operations and simplify information exchange and interoperability between currently fragmented systems; and (3) increase the stakeholders' knowledge of earthwork operations through the provision of the information, which is structured in the context of robust knowledge.

This paper proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases. EW-Onto represents the semantic values of the entities and the relationships, which are identified and formalized based on the basic definitions available in the literature and outlined by experts.

This paper aims to present an automated system that measures the volume of excavated soil in earthmoving operations. It focuses primarily on presenting the use of the radio frequency identification – real time location technology and image analysis techniques in isolation of stockpiles from their noisy backgrounds, extraction of their geometrical attributes and determination of their location.

An extensive literature review of image analysis techniques is performed to identify the ones most applicable in developing the proposed automated system. A set of techniques are selected and experimented to evaluate their effectiveness in the proposed application. A review of the state‐of‐the‐art distance measurement technologies is also conducted to identify the most suitable system to be used in the proposed system.

A set of image analysis techniques that positively contribute to the accuracy of the proposed system is identified. Geometrical attributes of stockpiles are automatically extracted for volume measurement purposes. The use of an affordable and reliable automated distance measurement tool is also suggested.

Although the proposed methodology is believed to be applicable to most configurations of stockpiles, it was tested on conical ones only.

The current practice of measuring the volume of excavated soil is time consuming and costly. Furthermore, it does not facilitate monitoring of excavation activities on close time intervals. The proposed system overcomes the problems associated with the current practice and provides an automated strategy that can be easily used by field personnel and/or home office management staff to closely monitor the progress of their earthmoving operations without physical human intervention.

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.

This paper describes the integration of various models to provide a realistic decision support system for linear project site layout. Initially, the paper describes an investigation to determine the actual methods currently used by project managers and planners. A review of both techniques adopted by the managers and the knowledge acquisition methods employed are included in the paper. Following this, this paper describes the work done to automate the existing systems. This resulted in a system which has been used in practice and has been shown to be a great help to managers. It is based on the traditional method of mass‐haul diagrams used to determine the earthworks activities. A separate simulation and artificial intelligence model of earthworks are described. This will be extended to model linear projects more realistically than does mass‐haul.

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).

Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging from classical mathematical programming to knowledge‐based expert systems (KBESs) have been applied to solve the function optimization problem, there still exists the need for improved solution techniques in solving the combinatorial optimization. This paper reports an exploratory work that investigates the integration of genetic algorithms (GAs) with organizational databases to solve the combinatorial problem in resource optimization and management. The solution strategy involved using two levels of knowledge (declarative and procedural) to address the problems of numerical function, and combinatorial optimization of resources. The research shows that GAs can be effectively integrated into the evolving decision support systems (DSSs) for resource optimization and management, and that integrating a hybrid GA that incorporates resource economic and productivity factors, would facilitate the development of a more robust DSS. This helps to overcome the major limitations of current optimization techniques such as linear programming and monolithic techniques such as the KBES. The results also highlighted that GA exhibits the chaotic characteristics that are often observed in other complex non‐linear dynamic systems. The empirical results are discussed, and some recommendations given on how to achieve improved results in adapting GAs for decision support in the architecture, engineering and construction (AEC) sector.

The purpose of this paper is to characterize the relationships between air pollutant emissions from heavy duty diesel equipment and highway construction project scope, schedule, and budget. Objectives included estimating total project emissions; developing a daily emissions profile; and developing new emissions estimating metrics based on project scope, schedule, and budget.

The research approach involved collecting real-world data related to project scope, schedule, and budget from two highway case study projects. The data were used to establish an emissions inventory estimating methodology to calculate total emissions for each case study. The total emissions were normalized based on project size, duration, and cost in order to develop new emissions estimating metrics.

The results proved that it is possible to characterize total equipment emissions based on project size, duration, and cost. The new emissions estimating metrics were quantitatively similar for both case studies.

The results were based on two case study projects. Additional data from more projects is needed to provide more highly refined numerical results.

This approach enables project planners and managers to assess the environmental impacts of highway projects along with the financial and time impacts.

Construction equipment is a major contributor to the nation’s air pollution problem. Before pollutant emissions can be managed they must first be measured.

The new emissions estimating metrics are a novel approach to comparing environmental impacts of two or more projects, as well as estimating total emissions for future highway construction projects.

Notwithstanding the Geographical Information System (GIS) being a fast-emerging green area of a digital revolution, the available studies focus on different subject areas of application in the construction industry, with no study that clarifies its knowledge strands. Hence, this systematic review analyses GIS core area of application, its system integration patterns, challenges and future directions in the construction industry.

A systematic review approach was employed, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. A total of 60 articles published between 2011 and 2022 were identified, thoroughly reviewed and analysed using thematic analysis.

The analysis revealed spatial planning and design, construction-task tracking, defect detection and safety monitoring as its four main application-based areas. The findings showed that the adoption of GIS technology is rapidly expanding and being utilised more in building projects to visual-track construction activities. The review discovered an integrated pattern involving data flow from a device and window-form application to GIS, the pathways to data exchange between platforms to platforms, where ArcGIS is the most used software. Furthermore, the study highlighted the lack of interoperability between heterogeneous systems as the crux impediment to adopting GIS in the built environment.

The research provides a deep insight into possible areas where GIS is adopted in the construction industry, identifying areas of extensive and limited application coverage over a decade. Besides, it demystifies possible pathways for future integration opportunities of GIS with other emerging technologies within the construction industry.

Computer simulation in construction planning has been the subject of research for the last few decades. The present paper describes simulation models geared toward improving the productivity of concreting operations. It is primarily concerned with a study of the sensitivity of concreting operations to a set of possible resource combinations. Thirteen models are examined by the two well‐known methods of concreting: (1) crane and bucket; and (2) the pump. Concreting into slabs, beams and columns are considered. The simulation software Micro‐CYCLONE is used for the actual generation of models. Sensitivity parameters considered in resource combinations include the number of truck‐mixers, buckets and labourers in concrete placing crews. The simulation models developed are compared and the results are discussed. The results enable planners to realize how the resource quantities and capacities in one cycle affect the ones in another period for cyclic operations like concreting. It can be concluded that the maximum number of resources, the interaction of work crews caused by work space limitations and the interaction of equipment because of sharing with other activities of the project may bring limitations.

This study aims to develop a framework for optimizing the spatial requirements of the equipment in a construction site using a geographic information system (GIS).

An ongoing construction project, an existing thermal powerplant in India, is considered to be the case study, and the corresponding construction activities were scheduled. The equipment spaces were defined for the scheduled activities in building information modelling (BIM), which was further imported to GIS to define the topology rules, validate and optimize the spatial requirements. The BIM simulates the indoor environment, which includes the actual structure being constructed, and the GIS helps in modelling the outdoor environment, which includes the existing structures, temporary facilitates, topography of the site, etc.; thus, this study incorporates the knowledge of BIM in a geospatial environment to obtain optimized equipment spaces for various activities.

Space in construction projects is to be considered as a resource as well as a constraint, which is to be modelled and planned according to the requirements. The integration of BIM and GIS for equipment space planning will enable precise identification of the errors in the equipment spaces defined and also result in fewer errors as possible. The integration has also eased the process of assigning the topology rules and validating the same, which otherwise is a tedious process.

The workspace for each activity will include the space of the equipment. But, in most of the previous works of workspace planning, only the labour space is considered, and the conflicts and congestions occurring due to the equipment were neglected. The planning of equipment spaces cannot be done based only on the indoor environment; it has to be carried out by considering the surroundings and topography of the site, which have not been researched extensively despite its importance.