Ghost: Project Network Generator

Navinchandra, D.; Sriram, D.; Logcher, Robert D.

doi:10.1061/(asce)0887-3801(1988)2:3(239)

Cited by 71 publications

(18 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gray (1986) identify sequence rationales such as "covered by" or "weather protected by other components" that are generalized from different contractors' schedules. Navinchandra et al (1988) identify similar dependencies as "supported by" and "connected to". Most of these sequence rationales are derived from the physical relationships between building components.…”

Section: Sequencing Of Construction Processesmentioning

confidence: 93%

“…As an extension to this, many researchers attempt to develop an Artificial Intelligent (AI) planner that not only store all the relevant sequencing rationales but also automatically apply this knowledge to identify construction sequence, e.g. OARPLAN (Darwiche et al 1988), GHOST (Navinchandra et al 1988), BUILDER (Cherneff et al 1991), and MDA (Jägbeck 1994). Aalami et al (1998) categorize these systems into two major groups: (1) process-based reasoning system, e.g.…”

Section: Sequencing Of Construction Processesmentioning

confidence: 99%

See 1 more Smart Citation

Pipe Spool Fabrication Sequencing by Automated Planning

Mohamed

2012

Construction Research Congress 2012

View full text Add to dashboard Cite

Construction of Industrial facilities involves a substantial amount of piping. Pipe spools are usually pre-fabricated from a number of raw pipes and pipe fittings (e.g. elbows, flanges, tees, etc.) in fabrication shops. Pipe spool fabrication is often affected by various disruptions from within or outside the shops. Previous research mainly focuses on shop layouts, dispatching rules, buffer location and standardized products. Another critical factor, the sequencing of pipe spool fabrication, is usually overlooked. A pipe spool can be fabricated in several alternative sequences that are often decided by shop foremen based on experience. It is rare that these alternative sequences get compared and evaluated. A simulation experiment shows that shop productivity can be improved by varying spool fabrication sequence. This paper presents an investigation of Artificial Intelligence (AI) planning approach that automatically identifies the optimal fabrication sequence for pipe spools while considering various fabrication logics. Experiments are conducted with different AI planners to evaluate their capabilities. The results indicate that one of the planners is more suitable for solving the sequencing problem than others. However, it requires special pre-processing of the input that may be prohibiting for practical use. Directions of future research to overcome these limitations are discussed.

show abstract

Section: Sequencing Of Construction Processesmentioning

confidence: 93%

Section: Sequencing Of Construction Processesmentioning

confidence: 99%

Pipe Spool Fabrication Sequencing by Automated Planning

Mohamed

2012

Construction Research Congress 2012

View full text Add to dashboard Cite

show abstract

“…In the late 1980s, Navinchandra et al [64] described their GHOST network generator. The GHOST was able to take activities as input and develop a precedency network for those activities as output, by relying on knowledge about construction rules, basic physics, etc.…”

Section: Related Research Studiesmentioning

confidence: 99%

Automation in construction scheduling: a review of the literature

Faghihi

Nejat

Reinschmidt

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Automating the development of construction schedules has been an interesting topic for researchers around the world for almost three decades. Researchers have approached solving scheduling problems with different tools and techniques. Whenever a new artificial intelligence or optimization tool has been introduced, researchers in the construction field have tried to use it to find the answer to one of their key problems-the "better" construction schedule. Each researcher defines this "better" slightly different. This article reviews the research on automation in construction scheduling from 1985 to 2014. It also covers the topic using different approaches, including case-based reasoning, knowledge-based approaches, model-based approaches, genetic algorithms, expert systems, neural networks, and other methods. The synthesis of the results highlights the share of the aforementioned methods in tackling the scheduling challenge, with genetic algorithms shown to be the most dominant approach. Although the synthesis reveals the high applicability of genetic algorithms to the different aspects of managing a project, including schedule, cost, and quality, it exposed a more limited project management application for the other methods.

show abstract

“…This can inhibit knowledge transfer and task automation efforts. The need for a clear and transparent modeling of construction constraints that affect the planning of a construction project has been an area of research for at least the past quarter of a century [ (Arditi and Rackas 1986), (Navinchandra, Sriram et al 1988), (Echeverry, Ibbs et al 1991), (Fischer and Aalami 1996)]. …”

Section: Related Workmentioning

confidence: 99%

Automated Generation of 4D Building Information Models through Spatial Reasoning

Weldu¹,

Knapp²

2012

Construction Research Congress 2012

View full text Add to dashboard Cite

Building information models (BIM), through their rich digital information content, have created an opportunity to automate portions of the estimating, scheduling, and visualization processes in construction management. In this research, we are developing a method that applies rule-based spatial reasoning based on the topological relationships between the building components in a BIM to automatically generate a technologically and physically meaningful construction sequencing of the building components while automating association of these activities with BIM objects for purposes of 4D schedule visualization. The spatial reasoning takes into account considerations such as structural construction, material layers and work access, some common construction practices and hierarchical relationships between the building entities. The methodology is applied to the domain of commercial construction projects. INTRODUCTIONThe process of producing working schedules in construction projects is a contextual matter pertaining to the unique project at hand. In the absence of visual aids, such as 3D architectural models, it would require the scheduler to summon their expertise and experience to visualize and mentally walk through the structure to be built, and hence, the process of its construction, to produce the actual schedules. Here, the 2D CAD drawings, contract documents, available resources of the contractor and other relevant data about the project are important inputs to determine the program of construction.These inputs, however, are not in a form that can be fed into a computer program which can apply algorithms to convert them into a schedule to manage the building process. Instead, the scheduler's power of visualization and experience is required to extract the necessary data from the model and prepare a schedule in a CPM format. This process is error prone, time consuming, and challenging especially for beginner professionals. Even after the schedule is created, years of practical experience is required to make full sense of all the information stored in the text and

show abstract

Ghost: Project Network Generator

Cited by 71 publications

References 6 publications

Pipe Spool Fabrication Sequencing by Automated Planning

Pipe Spool Fabrication Sequencing by Automated Planning

Automation in construction scheduling: a review of the literature

Automated Generation of 4D Building Information Models through Spatial Reasoning

Contact Info

Product

Resources

About