Purpose – The purpose of this paper is to present a newly developed fuzzy-set based model for estimating, allocating, depleting, and managing contingency fund over the life cycle of construction projects. Design/methodology/approach – Fuzzy set theory is utilized in the design and development of proposed contingency modelling framework to incorporate uncertainties associated with the development phases of construction projects. A set of developed indices, measures, and ratios are introduced to quantify and characterize these uncertainties. The developed framework is designed to incorporate expert opinion and provide user-system interaction. Findings – The results obtained from the application of the developed framework on actual project case not only illustrate its accuracy, but also demonstrate its capabilities for contingency management over life cycle of construction projects. Unlike other methods, the framework provides project managers with structured method for contingency depletion utilizing a set of depletion curves and selection factors. Originality/value – The novelty of the developed framework lies not only in its new developments for contingency estimating but also its modelling for contingency allocation and depletion. It is expected to be of direct value to industry professionals and academics interested in contingency management over the entire life cycle of construction projects. The proposed framework provides management functions and features beyond those generated through Monte Carlo simulation and even those developed using fuzzy set theory.
Scheduling of construction projects can be categorized into repetitive and non-repetitive. 47Repetitive scheduling contains cycles of repetitive activities such as those encountered in 48 housing units, highways, pipelines, and railways. Non-repetitive scheduling (network 49 scheduling) fits construction projects that do not require continuity of resources. These two types 50 of scheduling are also referred to as activity-based network scheduling and location-based Method 118The proposed method integrates CCPM and LSM; the aggressiveness of CCPM leads to shorter 119 schedules while LSM visualizes repetitive processes and accounts for continuity of resources. 120The framework of proposed method is illustrated in Figure ( 129Calculation of aggressive and safe durations using CPR leads to identification of the controlling 130 resource for each activity as presented in Equation (3). The controlling resource is defined as the 131 resource that controls the activity duration. Where, 138D i, j (AG), represents the aggressive duration of activity "i" in process "j". 139CPR i (CL), represents constrained productivity rate of activity "i" with resources availability at 140"CL" confidence level. 141CPRi (AG) represents constrained productivity rate of activity "i" with resources availability at 142 50% confidence level. Sequencing activities based on aggressive durations (average schedule):144The aggressive schedule is sequenced using illustrates the developed procedure for maintaining the continuity of resources. Russell and continuity of work for all activities "i" in the same process "j" as well as the continuity of work 160 among consecutive processes using Equations (4) and (5). Equation (4) calculates the SD of an 161 activity "i" as the maximum between the finish date (FD) of same activity "i" in preceding 162 process "j-1" and the FD of its preceding activity "i-1" in the same process "j" to maintain 163 continuity in the same process as well in the consecutive processes. Equation (5) calculates the 164 FD of activity "i" in process "j" as the sum of SD ij calculated using Equation (4) and the duration 165 of activity "i" in process "j" calculated using Equation (1) and (2). As long as "j" remains 166 smaller than the total number of processes (m), this procedure is repeated for all activities "i" in 167 all processes "j" to maintain the continuity of processes in the developed linear schedule. However, the continuity constraint in linear scheduling should be respected. If two or more 179 sequential activities share the same controlling resource, then the priority is given to predecessor 199QC: Resources quantities consumption during normal duration in preceding activity. 200O RC : Overlap due to resources conflicts. 201The variability of resource for preceding activity shifts the start of the succeeding activity by Where, 210O VAR : Overlap of resources quantities variability for preceding activity. 211Q 50%: quantities in preceding activity with 50 % confidence in availability. 212Q 90%: quantities in ...
Considerable work has been carried out on risk qualitative and quantitative assessment but far less on risk identification. This paper introduces newly developed method for risk identification, based on micro risk breakdown structure (MRBS) and newly introduced identification procedure called preventive root cause and effective remedial (PRCER). It also introduces a risk responsibility matrix (RRM) that distributes the responsibilities associated with each risk among project stakeholders and introduces a newly developed method for qualitative and quantitative assessment of each item using fuzzy set and fuzzy probability theories. Output of the proposed assessment method is pre-mitigation contingency (PREMC) of each risk which represents a quantitative indicator for decision making whether to mitigate or not the risk being considered.Two case studies and one numerical example are presented to demonstrate the applicability and illustrate the essential features of proposed identification, allocation and assessment methods. Risk register items at task level are evaluated, qualitatively and quantitatively, using of fuzzy set theory (Zadeh 1965) and fuzzy probability theory (Zadeh 2008). Risk assessment team should be subdivided into subgroups, based on their experience, to evaluate risk consequences, and
Modular construction enables delivery of a building as an assembly of a set of modules manufactured offsite in a controlled manufacturing facility environment. Unlike stick-built practices, modular construction enables higher schedule control of construction projects due the inherent concurrency of offsite and onsite construction operations. Literature provides simulation-based scheduling methods that integrate offsite and onsite construction activities. These methods, however, depend largely on availability of data such as productivity rates for offsite and onsite activities. This paper presents an alternative BIM-based framework that integrates linear schedules of onsite and offsite construction operations in a manner that synchronizes work progress of these operations. The proposed framework considers limited capacities of storage areas in the manufacturing facility and on site as well as, the availability of trucks for delivering the fabricated modules from manufacturing facility to the jobsite. The use of BIM provides visualization capabilities for the integrated schedule and allows for monitoring simultaneously the work progress of offsite and onsite activities. Conclusions are drawn concerning the suitability of developed framework for integrated scheduling of modular construction projects. KeywordsBIM, Integration, Scheduling, Modular Construction. IntroductionA recent survey of 800 engineers, architects, and contracting professionals reveals modular construction advantages including shorter project schedules (66% of respondents); lower cost (65% of respondents); and reduced construction waste (77% of respondents) [1]. Independent KPMG research found that financial net savings for offsite construction projects are 7% due to shortened construction period without considering the savings generated from decreasing the interest of borrowing [2]. These savings enable faster rental income and lower escalation in construction costs. The combination of offsite and onsite construction in a "50-storey office building" project in central London generated combined savings of £ 36 million [2].Offsite construction provides other benefits such as; enhanced predictability of time and cost, reduced noise from construction, and improved health and safety. According to size and complexity of manufactured components, offsite construction types are grouped into five categories; 1) modular, 2) hybrid, 3) panelized, 4) prefabricated components, 5) processed material [3]. Modular construction reduces considerably the schedule of construction projects which may generate significant cost savings. Literature reviewParallel scheduling for offsite and onsite construction schedules saves 30 to 50 percent of project duration as compared to stick-built traditional construction processes as shown in Figure 1
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