China
Africa
Explore chapters and articles related to this topic
Application
Published in Andrew Braham, Sadie Casillas, Fundamentals of Sustainability in Civil Engineering, 2020
Depending on the project, there could be more or less content than listed in these six sections, but this provides the general scope of a project manual. MasterFormat comes into play in the sixth section – construction products and activities. Obviously, there are literally thousands of construction products and activities on any given job site, so having a logical and consistent presentation of information is tremendously helpful. MasterFormat is divided into 48 divisions under five subgroups: General requirements (Division 01)Facility construction (Division 02–14) (handout, .pdf p. 22-23)Facility services (Division 21–28)Site and infrastructure (Division 29–35)Process equipment (Division 40–48)
Capital Cost Estimating An Overview
Published in Kenneth K. Humphreys, Paul Wellman, Basic Cost Engineering, 1995
Kenneth K. Humphreys, Paul Wellman
Construction projects are designed and built by multidisciplined teams of experts representing diverse products and systems. Communication is very important between these experts, and the language must be understood by all. To facilitate this communication, MasterFormat has been established by the Construction Specifications Institute (CSI). Quoting CSI:
Construction Management
Published in Abdul Razzak Rumane, Handbook of Construction Management, 2016
The designer/consultant is responsible to prepare detail specifications and contract documents that meets the owner’s needs, and specifies the required level of quality, schedule, and budget. Prepare specifications: Specifications of work quality are an important feature of construction project design. Specifications of required quality and components represent part of the contract documents and are detailed under various sections of particular specifications. Generally, the contract documents include all the details as well as references to generally accepted quality standards published by international standards organizations. Proper specifications and contract documentations are extremely important as these are used by the contractor as a measure of quality compliance during construction process.Particular specifications consist of many sections related to specific topic. Detailed requirements are written in these sections to enable contractor understand the product or system to be installed in the construction project. The designer has to interact with project team members and owner while preparing the contract documents.Generalized writing of these sections is as follows: Prepare contract documents: Preparation of detailed documents and specifications as per MasterFormat is one of the activities performed during this phase of the construction project. The contract documents must specify the scope of works, location, quality, and duration for completion of the facility. As regards the technical specifications of the construction project, MasterFormat specifications are included in the contract documents. Normally, construction documents are prepared as per MasterFormat contract documents produced jointly by the CSI and CSC are widely accepted as standard practice for preparation of contract document.MasterFormat is a master list of section titles and numbers for organizing information about construction requirements, products, and activities into a standard sequence. MasterFormat is a uniform system for organizing information in project manuals, for organizing cost data, for filling product information and other technical data, for identifying drawing objects, and for presenting construction market data. MasterFormat 2014 edition consists of 48 divisions (49 is reserved). For more information, see Chapter 6.
Multi-model probabilistic analysis of the lifecycle cost of buildings
Published in Sustainable and Resilient Infrastructure, 2022
Stevan Gavrilovic, Terje Haukaas
where CSI stands for MasterFormat codes from the Construction Specifications Institute (CSI, 2018), which are unique numbers used to identify products and activities within the construction industry, K = total number of labour tasks, each corresponding to a CSI code, = labour-hours required for a crew to complete one unit of task k, and = number of units of task k. The labour-hours and crew type are provided by RSMeans (RSMeans, 2019), as shown in Table 2. Crews can be reused for multiple tasks, but the labour-hours are specific to each CSI task. To address the uncertainty in the calculation of the total labour-hours for a project, the labour-hours needed to complete a task are imported into Rts as Lognormal random variables. The mean values of the random variables are the labour-hours given by RSMeans and a 30% coefficient of variation is assumed.
A framework of developing machine learning models for facility life-cycle cost analysis
Published in Building Research & Information, 2020
Xinghua Gao, Pardis Pishdad-Bozorgi
This research has two major limitations. First, the case study used for validating the proposed LCC framework was limited to developing machine learning models for overall LCC predictions during the programming phase. This framework is applicable to all building design, construction, and facilities management phases, and machine learning models for analysis of a building's LCC can be developed as soon as the relevant data become available. The models developed in this experiment can only predict the lump sums of the initial cost, utility cost, and O&M cost, respectively. With more detailed building cost data, such as the cost breakdown according to CSI MasterFormat structure or UniFormat structure, machine learning models for more detailed cost estimation could be developed based on the proposed framework. Second, no benchmarking tool (the baseline) was available to evaluate the improvements in prediction accuracy provided by the developed models. The studied university did not have a prediction tool to use during the programming phase. The university's budget planning and administration department, and facilities management departments had not used the historical data for building LCC predictions before. Typically, cost estimators are hired to perform the initial cost prediction, but these data were not available to compare with the predictions produced by the developed machine learning models. Moreover, the utility and O&M cost estimation did not have a comparison base, because the estimation of these costs, if any, is typically conducted after the design has become available. The stakeholders in the university lacked a viable tool to conduct LCC analysis of utility and O&M costs during the programming phase.