'Design clashes' encountered during the development of a large multi-storey educational building, 27 awarded under a Joint Contracts Tribunal (JCT) Design and Build contract, are reported upon. The 28 building was developed in Birmingham, UK and the contract value was circa £36 million (UK 29 Sterling, 2015). Members of the project management team (PMT) produced designs that were 30 subsequently integrated by the main contractor into a federated building information modelling 31 (BIM) model; at this stage 404 error clashes were evident between the positions of the mechanical, 32 electrical and plumbing (MEP) designer's and structural designer's building compartments. The 33 contractor deemed that these particular clashes were 'mission critical' as previous experience 34 suggested that project costs could spiral uncontrollably if left unabated. Participatory action 35 research was employed to acquire a deeper understanding and knowledge of the clash incidents. 36 Clash data accrued (in mm) was subsequently quantitatively modelled using the probability 37 density function (PDF) and the cumulative distribution function (CDF). Two models produced 38 were the Log Logistic Three Parameter (3P) (using all data including outliers) and Generalized 39 Gamma distribution (excluding outliers). Both models satisfied Anderson-Darling and 40 Kolmogorov-Smirnov goodness of fit tests at 0.01 and 0.02 levels of significance. Model 41 parameters could be used to forecast similar clashes occurring on future projects and will prove 42 invaluable to PMT members when accurately estimating the time and resource needed to integrate 43 BIM designs. The predictive modelling revealed that 92.98% of clashes reside within the 30-299 44 mm range while the most probable occurrence of a clash overlap resides in a discrete category of 45 100-199mm. Further qualitative investigation is also conducted to understand why these clashes 46 occurred and propagate ideas about how such may be mitigated. The research concludes on two 47 important points, namely: i) BIM is not a panacea to design related construction project rework 48 and that innovative 21 st century digital technologies are hampered by 20 th century management 49 practices; and ii) improvements in clash and error mitigation reside in a better understanding of 50 tolerances specified to alleviate the erroneous task of resolving unnecessary clashes. Future 51 research is proposed that seeks to: automate the clash detection management, analysis and 52 resolution process; conduct further investigative analysis of the organizational and human resource 53 3 management influences impacting upon design clash propagation; and devise and validate new 54 procedural methods to mitigate clash occurrence using a real-life project. 55 56 KEYWORDS 57 Building information modelling, clash detection, probability density function, cumulative 58 distribution function, Generalized Gamma distribution, Log Logistic (3P) distribution. 59 60 61 The digital jacquerie transcends the narrow confines of the information and communication 62 technology sector and is ubiquitous throughout all industry (Edwards et al., 2016). This paradigm 63 shift in business and commerce has been enabled through the application of cloud computing (Park 64 and Ryoo, 2013). Cloud computing is advantageous to all organizations (large and small) because 65 utilizing internet-based services can reduce start-up costs, lower capital expenditures and increase 66 computational power to augment business/ market intelligence (Chen and Lin, 2012). A menagerie 67 of 'networked' digital devices employed within the workplace generate vast quantities of data, 68 information and knowledge that can be further exploited via automated and intelligent analytics 69 (Dutta and Bose, 2015). Business intelligence and concomitant data analysis have the inherent 70 potential to uncover patterns, trends and associations related to design data, human behavior, and 71 the interactions between the two, for improved decision making (Manyika et al., 2011; Russom, 72 2013). Indeed, the extant literature postulates (cf. Shollo and Galliers, 2016; Seddon et al., 2016) 73 that business intelligence enables organizations to gain value from business analytics. 74 Multitudinous benefits of digitization have similarly been promulgated within the architecture, 75 engineering, construction and owner-operated (AECO) sector (Love et al., 2015). Prominent 76 digital technologies include: sensors (Park et al., 2016); laser scanners (Oskouie et al., 2016); 77 machine vision (Teizer, 2015); and building information modelling (BIM) (Ben-Alon and Sacks, 78 2017). Amalgamated, these technologies have spearheaded the advancement of the digital 79 construction modus operandi (Zhou et al., 2012). BIM is ostensibly the most prevalent of these 80 advanced technologies within extant literature and is gradually becoming conventional in both 81 design and construction practice globally (Liu et al., 2016). BIM provides a digital portal through 82 which an integrated project management team (PMT) can collaboratively work upon, and share 83 knowledge of, a construction or infrastructure development pre-, during and post-construction 84 4 (Ciribini et al., 2016; Wetzel and Thabet, 2016). This innovative approach enables PMT members 85 to enhance their inter-disciplinary interactions in order to optimize resultant decisions and afford 86 greater whole life value for the asset (Love et al., 2016). 87 88 During the design stages of pre-construction, BIM drawings and plans produced by individual 89 designers (e.g. the architect, structural engineer and mechanical, electrical and plumbing (MEP) 90 designer) are integrated into a federated model and tested to identify design clashes (Bagwat and 91 Shinde, 2016). Design clashes consist of 'positioning errors' where building components overlap 92 each other when the original individual designer models are merged. Resolving these design 93 clashes is imperative to project performance, particularly if costly rework is to be circumvented 94 during the construction phase. However, design clash mitigation and the utilization of 95 deterministic modelling to enhance decision making are two areas that have been grossly 96 overlooked within the literature (Won and Lee, 2016; Jones and Bernstein, 2014). Given scant 97 research within this important area and the opportunity to improve construction business 98 performance, this work reports upon the findings of participatory action research (PAR) which 99 sought to examine design error clashes that occurred during the compilation of a federated BIM 100 model for a multi-storey educational building development. Such work provides invaluable insight 101 into a previously unexplored area of digital built environment research. The research objectives 102 are to: better understand why clashes occur and engender wider academic debate; demonstrate 103 how the probability density function (PDF) and cumulative distribution function (CDF) can 104 accurately predict the probability of future occurrence for a specific project; formulate innovative 105 ideas for reducing their occurrence and mitigating their impact upon construction business 106 processes and performance; and suggest future work that seeks to maximize business intelligence 107 through automation and apply the deterministic techniques adopted to a larger number of project 108 developments as a means of generalizing the findings. 109 110 DESIGN ERRORS WITHIN DIGITAL CONSTRUCTION 111 Design errors are a prominent root cause of diminished construction project performance and 112 manifest themselves as adverse symptoms such as: rework (Lopez et al., 2010; Li and Taylor, 113 2014; Love, and Sing, 2013); cost overruns (Love et al., 2014; Love et al., 2013); schedule delays 114 (ibid); and unsafe working environments (Love et al., 2010). Literature proffers that the main 115 5 sources of design error are inextricably linked to iterative and recurrent design cycles that result 116 from: unanticipated changes (Lee et al., 2005); poor management and communication (Arayici et 117 al., 2012); realignment of traditional/ institutionalized organizational and human resource 118 practices (Porwal and Hewage, 2013); and interoperability between various software platforms 119 (Merschbrock and Munkvold, 2015). These challenges have engendered frenzied research activity 120 and resulted in the: development of system dynamics models for planning and control (Lee et al., 121 2005); identification of critical design management factors (Whang et al., 2016); and examination 122 of causal factors (Forcada et al., 2016). Despite this herculean effort, anecdotal evidence from 123 industry reveals that design errors remain a persistent problem. 124 125 BIM offers a potential digital solution space for design error management as a collaborative and 126 inclusive platform (Solihin et al., 2016). Yet to date, limited research has investigated whether 127 BIM in the AECO sector is effectively mitigating digital design errors. Love et al., (2010) further 128 proffer that the process of design error mitigation implies that: 129 130 "…learning from errors is a collective capacity that can produce individual, 131 organizational, and interorganisational error prevention practices." 132 133 Successful error mitigation should therefore nurture learning from within individual design 134 disciplines to encapsulate the entire project team (ibid). BIM inherently offers this potential but as 135 the first stage of design error mitigation, clash detection and consequential resolution between 136 design team members has received scant academic attention. Amongst the various structural 137 elements, MEP design errors have traditionally dogged the design process, arguably due to the 138 confined spaces left for MEP systems (Tatum et al., 1999). Recent research conducted by 139 Peansupap and Ly (2015) examined five categories of structural and MEP related design errors, 140 but the study was confined to schedule delays and omitted any discussion on how BIM can 141 facilitate error mitigation at the detailed design stages. Research that has examined design clashes 142 in a BIM environment remains anecdotal or based upon a limited scope of analysis (Al Hattab and 143 Hamzeh, 2015; Allen et al., 2005; Won and Lee, 2016). 144 Clash Reports and Nomenclature 145 6 When reporting upon design clashes, the main contractor produces periodic clash detection reports 146 that contain information including: i) thematic groupings of clashes that report upon individual 147 clashes within each compartment category (for example, and in this research 'MEP vs building 148 column' and 'MEP vs building frame'); ii) snapshots of every clash identified to aid 149 communication with all designers throughout the PMT; iii) clash point co-ordinates (as x, y and z 150 coordinates) to determine the exact pin-point location of the clash within the federated BIM model; 151 iv) the date that the clash was found; v) clash status (active and unresolved or resolved); vi) a 152 written description of the clash; and vii) a numerical value in metres (m) or millimetres (mm) that 153 specifies the linear magnitude of the positional (clash) error. Manual data cleansing is then 154 undertaken by the contractor's BIM manager using industry nomenclature to define four key clash 155 categories , namely: i) clash errors -fault clashes that must be identified and resolved within the 156 federated model; ii) pseudo clashespermissible fault clashes that can be tolerated within the 157 design and do not require resolution; iii) deliberate clashesintentional clashes, for example, 158 ducting through a floor or web of a structural steel component; iv) duplicate clashesmultiple 159 versions of the same 'singular clash' that are repeated throughout a building (e.g. an MEP pipe 160 that travels along the entire length of a structural column will be observed and recorded numerous 161 times even though it actually represents one error). Duplicate clashes often originate from one of 162 the three other variants of clash. 163 164 RESEARCH APPROACH 165 The research design employed participatory action research (PAR) (cf. Chevalier and Buckles, 166 2013; Smith et al., 2010) where the lead researcher was embedded within, and worked closely 167 with, the PMT to develop various aspects of the BIM model. The PMT included the client's 168 representatives (i.e. the building's estates department) and design related disciplines (including 169 the BIM process manager, the lead architect, contractor's construction manager, the contractor's 170 BIM manager, principle designer for mechanical engineering and plumbing and the lead structural 171 engineer). Note that the estate's department held four fundamental roles, namely that of: client's 172 representative; BIM process manager; project manager; and estates department and consequently, 173 covered all three major phases of the building's life cycle. PAR was adopted because it offers 174 pluralistic orientation to knowledge creation and change thus affording greater flexibility to 175 excoriate beneath the corporate façade that can obscure truth in the interests of preserving 197 the 'Mary Seacole Building'refer to Figure 1 for external visualization) provided the contextual 198 setting for the research. The contract value exceeded £36 million UK Sterling and created 10,000 199 sq m of new teaching space. The project commenced with a client sign off on March 2015 and is 200 currently ongoing with an expected handover to client by September 2017. A Joint Contracts 201 Tribunal (JCT) Design and Build was employed and procurement was implemented via the 202 Official Journal of the European Union (OJEU) tender submissions. OJEU is used for all tenders 203 from the public sector which are valued above a certain financial threshold according to European 204 Union legislation (Lam, 2016). 205 ANALYSIS 206 8 A federated BIM model was used to identify clash detections. Federated models are deployed 207 using various BIM-related platforms including: Bentley Navigator®, Autodesk Navisworks® and 208 Autodesk Glue®. For this research, Autodesk Glue® was used to facilitate cloud based model 209 federation. The project employer information requirements stipulate that for the contractor: 210 211 "Glue Coordination models will be created at different stages. They will be used for a 212 number of reasons, some of these are, clash detection, MDM creation, 4D and 5D 213 modeling, and used as the base model for the 'BIM 360 Field' databasethese are but 214 some of the uses." 215 216 The main contractor employed a permanent BIM manager to manage clash detection of the 217 federated model in Navisworks® (refer to Figure 2). Spatial coordination between the various 218 design discipline models was carried out at regular fortnightly intervals (every ten working days) 219 throughout the design and construction stages. The BIM manager was integral within this process 220 and facilitated regular co-ordination of team meetings, model updates, clash revisions and control. 221 Clash detection in BIM is a global phenomenon; unlike other countries worldwide, it has been 222 forcefully mandated in the United Kingdom (UK) (HM Gov 2012, HM Gov 2013). According to 223 the UK Government mandated BIM Level 2 requirements, design teams must undertake weekly 224 or fortnightly task information and clash rendition tasks to ensure designs are fully coordinated 225 and clash free, ensuring that requests for information are minimised during construction stages 226 (HM Government, 2012; 2013). This government intervention seeks to mitigate design error 227 prominence within BIM implementation. The client also required that the main contractor 228 employed a clash detection management process on a fortnightly basis. Clash detection resolution 229 was implemented via Virtual Design and Construction (VDC) coordination meetings with the 230 respective design teams. The BIM execution plan (as outlined by the main contractor during 231 tender) stated that: 232 233 "The aspiration is that beyond Stage 4, the model will be managed by the principal 234 contractor and modifications to the model be made in house or by the design team. 235 Throughout the project the BIM lead from each company and the soft landings champion 236 will attend regular VDC coordination meetings. Efforts will be made to coordinate the The hypothesis regarding the distributional form is rejected at the chosen significance level (α) if 328 the statistic D and, A 2 are greater than the critical value. For the purposes of this research, 0.01, 329 0.02 and 0.05 significance levels were used to evaluate the null hypothesis. 330 331 The p-value, in contrast to fixed α values, is calculated based on the test statistic and denotes the 332 threshold value of significance level, in the sense that Ho will be accepted for all values of α less 333 than the p-value. Once the 'best fit' distribution was identified, the probabilities for a design 334 clashes were calculated using the CDF. 335 336 Distribution Fitting: Probability of the Size of Clash -Model One (All Data) 337 All 404 data points were analyzed for model one. Results reported in Table 2a illustrate that the 338 best fit probability distribution for the size of clash detections was the Log Logistic Three 339 Parameter (3P) at α = 0.01 and 0.02 confidence intervals; notably, the fit was not achieved at α = 340 0.05. The three parameters are: 341 342 = 2.2943; = 147.33; and = 23.249 343 344 The PDF (Figure 4) and CDF (Figure 5) for the Log Logistic 3P distribution fitting are defined in 345 equations 7 and 8 respectively as: 346 347 ( ) = ( − ) −1 (( − ) ) −2 [Eq.7] 348 349 ( ) = (1 + ( − ) ) −1 [Eq.8] 350 351 Where: is a continuous shape parameter with > 0; is a continuous scale parameter with 352 > 0; and is a continuous location parameter where ≡ 0 yields the two parameter-Log 353 Logistic distribution. The domain for this distribution is < < +∞. 354 355 Distribution Fitting: Probability of the Size of Clash -Model Two (Outliers Excluded) 370 371 Where: is a continuous shape parameter > 0; is a continuous shape parameter > 0; is 372 a continuous scale parameter > 0; and is a continuous location parameter ( ≡ 0 yields the 373 three-parameter Generalized Gamma distribution). Both distribution fitting models illustrate a good fit at the 0.01 and 0.02 confidence intervals and 376 therefore the removal of outliers was not a prerequisite requirement to obtaining a valid result. Using the parameters contained within model two, delimiters (X1 and X2) were used to calculate 378 the probabilities of obtaining a discrete category of clash ranging from 30-99mm, 100-199mm, 379 200-299mm, 300-399mm and 400-470mm (refer to Table 3). These tolerance categories were 380 defined and delineated by the contractor for the purposes of clash detection. The research team 381 felt that such: i) was an arbitrary decision inordinately influenced by a hired BIM consultant; and 382 ii) lacked logic and a meaningful basis for this decision. From this discrete analysis, it was apparent 383 that 92.98% of clashes reside within the 30-299 mm range; where this range consists of the 30-99 384 mm = 19.85%; 100-199 mm = 51.05%; and 200-299 mm = 22.08% discrete categories. 385 386 14 CLASH MANAGEMENT CHALLENGES AND CONSIDERATIONS 387 The quantitative analysis conducted within this research illustrates that PDF and CDF can 388 successfully model the probability of design clashes that occur during the development of a 389 federated BIM model. Such modelling will prove useful to the client and members of the design 390 team who seek to better understand and mitigate future clash occurrence. However, the origins of 391 clashes cannot be explained by quantitative analysis alone, hence further qualitative investigation 392 of the model federation and clash management process was conducted (refer to Figure 8). A three 393 tier process was implemented that consisted of: tier onethe design stage; tier twocloud 394 computing; and tier threeclash detection. During tier one, the architects, MEP designers, 395 structural engineers and other design consultants populated BIM semantic data within a discipline 396 specific BIM model in an iterative manner. These discipline specific models were then integrated 397 into an initial federated model. Tier two involved the implementation of the contractor's cloud 398 computing solution that provided a two-way communication portal between the designers and 399 contractor. Within the cloud, Autodesk Glue® was used to federate the model; BIM 360 Field was 400 used to store and upload site photographs and facilitate communication between individual PMT 401 members; and BIM 360 Layout was used as a tool to input Cartesian coordinates (of the building 402 and site) using a total station. In tier three, the contractor, contractor's BIM Manager and designers 403 implemented a recurrent process of clash detection and resolution. The designers identified model 404 clashes as a first step towards developing resolved model clashes that were uploaded into an initial 405 clash report. The contractor's BIM Manager then used this clash report to iteratively work with 406 designers to resolve clashes within a final federated model that was uploaded into the cloud for all 407 members of the PMT to access. This clash management process was further explored using 408 unstructured interviews with members of the PMT and highlighted several important challenges 409 facing practitioners working within a digital construction environment. These challenges can be 410 conveniently grouped into the following thematic groupings, namely: organizational influences; 411 manpower and training; automation of analysis (machine learning); and cross industry knowledge 412 transfer. 413 414 Organizational influences 415 BIM has been heralded as a 21 st century innovation that will not only improve the efficiency of 416 geometric modelling of a building's performance but also the management of construction projects (Bryde et al., 2013). Other researchers eulogize over BIM virtues pertaining to: energy savings 418 and concomitant cost reductions (Guo and Wei, 2016); greater control of the design, construction 419 and operation of an asset throughout its whole life cycle (Azhar, 2011; Wong and Zhou, 2015); 420 and significant time savings in the production process and consistency of the product (Arayici et 421 al., 2011; Ham and Golparvar-Fard, 2015). However, the research presented here observed that a 422 singular PMT is neither cohesive nor unified and consists of disparate teams working together to 423 populate the federated BIM model. Moreover, the mechanistic manner via which clashes were 424 identified and resolved afforded limited opportunity for members of the PMT to learn from 425 mistakes made by maximizing upon readily available business intelligence. This problem is further 426 exacerbated by software and model exchange issues when different members of the PMT work on 427 design work sets in isolation; a member of the PMT said: 428 429 "For example, the structural engineer could do a lot of work and not tell the architects 430 about it. This might happen, then both could upload their model into a centralised 431 location and now we have multiple clashes because the architects did not update their 432 model and the structural engineer has now done some changes to the steel frame." 433 434 This finding concurs with earlier research conducted by Porwal and Hewage (2013) who reported 435 that organizational and people centered issues pose the greatest challenge for BIM 436 implementation. Other organizational issues relate to intellectual property (IP) rights particularly 437 for architectural designs; a member of the PMT said: 438 439 "They [architects] are still failing to produce a coordinated design even though they are 440 sitting next to each other [with other design members in the PMT]. This is all about 441 intellectual property [IP] rights. Because of the IP, the architects that own the model 442 don't want you to easily edit it, so for example when you ask them for the Revit file they 443 will refuse to share it. This is because models are easily editable in Revit (you can design 444 in Revit) and once they give you a Revit model you can copy it and paste it somewhere 445 else. And they [architects] can charge you for it…" 446 447 16 Cumulatively, these improvised communication, organizational and administrative arrangements 448 make clash eradication per se difficult within a BIM environment particularly when a silo 449 mentality prevails. 450 451 Manpower, Training and Competence Development 452 Prior research (Succar et al., 2013; Murphy, 2014) advocates that professionals within the PMT 453 must develop core BIM competencies in order to secure performance improvement. Such 454 improvement could be achieved via organizational learning that seeks to create, retain and transfer 455 knowledge within an organization (Duffield and Whitty, 2016). The research presented, provides 456 an opportunity for sharing knowledge through the exploitation of business intelligence and 457 experiential learning amongst members of the PMT (Konak et al., 2014). However, organizational 458 learning is hampered within industry by the exponential rate of software-hardware technology 459 development and the concomitant need to continually retrain personnel to remain at the forefront 460 of knowledge and developments (Eadie et al., 2013). Evidence accrued from this research supports 461 this assertion and suggests that some members of the PMT have deliberately created a pretense of 462 full BIM compliance, when in fact their approach is compromised by ad hoc arrangements. A 463 member of the PMT said: 464 465 "It's all about knowledge, how the software is used. At the moment a lot of the 466 consultancies are running away with BIM, where they are just modelling using the CAD 467 drawings. Rather than using a proper BIM draughtsman, they employ a Revit technician. 468 The Revit technician receives CAD drawings and redraws these into Revit, which is not 469 a collaborative way of working. The structural engineer is doing all the calculations and 470 measurements in the CAD drawings in 2D and then this is being transferred into 3D with 471 errors!" 472 473 Evidence suggests that a huge BIM knowledge gap has developed between senior professionals 474 (architects, MEP designers, etc.) and small to medium enterprises (SMEs) that is compounded by 475 innate skill limitations (Harris et al., 2013). SMEs are quintessentially important as their services 476 are often used in the design, construction and/ or maintenance of buildings (Khan et al., 2016). Higher education institutes (and other education providers) must collaborate more closely with 478 17 these practitioners to fully embrace the concept of a 'life-long learner for digital construction' in 479 order to avoid tacit knowledge redundancy within SMEs. Automation of Analysis (Machine Learning) 482 Machine learning (ML) has its entomological roots grounded in artificial intelligence (AI) and 483 embraces computer learning without explicit programming (Bottou, 2014). ML focuses on the 484 development of computer programs that can teach themselves to grow and change when exposed 485 to new data (Perlich et al., 2014). Within the AECO sector, ML is already being used to: monitor 486 construction progress using 4D BIM (Golparvar-Fard and Han, 2015; Son et al., 2015); automate 487 rule checking within BIM models (Solihin and Eastman, 2015); automate as-built 3D 488 reconstruction using computer vision (Fathi et al., 2015); and monitor construction performance 489 using still images (Yang, 2015). However, despite these significant advances, clash detection 490 remains a laborious, mechanistic, time consuming and costly exercise. Each and every clash must 491 be manually integrated, analyzed and accessed by the BIM manager to first determine the type of 492 clash (i.e. clash errors, pseudo clash, deliberate clash or duplicate clash) before taking suitable 493 action and monitoring progress where a resolution is required. Automated methods are urgently 494 required to: rapidly assimilate vast quantities of geometric data accessed from a larger range of 495 construction and civil engineering projects to build accurate benchmark clash detection profiles 496 that could inform future decision making; define and delineate between the various clash types to 497 provide greater business intelligence regards which clashes require resolution thus eliminating the 498 need for manual intervention; and eliminate the need for manual intervention and the introduction 499 of human errors or omissions. 500 501 Cross Industry Software-Knowledge Transfer 502 In other more technologically advanced industries (e.g. automotive and aerospace), software 503 exchange file formats have been standardized to aid communication between various designers 504 and manufacturing production processes (Eastman et al., 2011). Within the AECO sector the BIM 505 authoring platforms adopted lack standardized user interfaces and file formats in an open 506 architecture environment. Although the Industry Foundation Classes (IFCs) specification sought 507 to alleviate these issues, anecdotal evidence from practitioners suggests that IFCs are not error 508 18 free. For example, geometry and semantic information can disappear when file formats are 509 exported from the original BIM authoring platform. A member of the PMT said: 510 511 "… many companies and consultancies are reluctant to give us the Revit files. That is 512 why the IFC was invented and generated, to allow for the export from any piece of 513 software. This was the holy grail of the BIM model, that you can export into a single 514 format which can be opened by any company or any BIM software vendor and federated 515 in IFC's. But obviously software vendors [vendor name removed] are failing to produce 516 usable IFC's, so it's very hard to export correct IFC from Revit. For example, today I 517 received some export IFC's from a vendor [vendor name removed] and they are coming 518 out with strange geometries that are not meant to be in the model." 519 520 Currently, there is no commercially available cloud-based BIM authoring platform that allows 521 designers to work collaboratively. As an exemplar of contemporary industry practice, members of 522 the project design team worked within separate BIM authoring platformsfor instance, the 523 architect used REVIT, the structural engineer used Tekla and MEP used REVIT MEP. These 524 various software packages, processes and procedures have been developed organically and 525 iteratively to meet industry needs but as yet, a single system that encapsulates holistic coverage 526 has eluded the sector. This is most likely because platform design specifications are often ill-527 defined, frequently complex and involve iterative processes, and user needs and specifications 528 evolve as the temporal and recurrent 'design to user-experience' process consolidates into an 529 optimal product solution (Chandrasegaran et al., 2013). A member of the PMT said: 530 531 "BIM 360 Glue allows you to view and federate the models from different consultants. So 532 for instance, I am getting uploads of the latest models to the single cloud storage to check 533 them. But I am also coordinating them, so all the clashes which should not be there, are 534 there to be checked by myself and my colleagues. Because the designers have been 535 working within their own silos and then just upload the models into the cloud based 536 platform for a clash detection." 537 Working from a cloud would alleviate many of the problems and issues faced when working in a 538 multi-disciplinary team where software and hardware requirements fail to synergize and often 539 19 require frequent annual updates. Annual updates in a cloud would ensure that all team members 540 are using the most up to date version. One common cloud-based modeling platform would provide 541 an ideal solution but agreement between five or more software providers of alternative platforms 542 could be problematic particularly on commercial grounds. A potential solution would be to 543 eliminate errors within IFCs and ensure ever-greater interoperability between software vendors -544 transference of best practice from more technologically advanced sectors could present an ideal 545 solution to this conundrum. A member of the PMT said: 546 547 "The guys [contractors] internally have got their heads around it [cloud based app] 548 because there are a lot of changes. So over the course of the year the site team has 549 changed slightly. Traditionally, there would be a lot of information that is stored on 550 emails, although they were sitting next to each other and talking with one another… 551 Because all of the issues have been raised on the iPads [on cloud] they are already there 552 for the next site manager to find. So at least they're not completely blind when they have 553 to come in to resolve the issues." 554 555 CONCLUSIONS 556 Despite the euphoria that often surrounds digital construction within extant literature, this research 557 has shown that BIM is not yet a panacea to mitigating design errors. Rather the nature of design 558 error propagation has changed and evolved in parallel with 'new technologies' applied that are 559 being managed by 'traditional management' processes and procedures. In addition, a distinct lack 560 of organizational learning within the PMT was evident and so the opportunity to secure 561 experiential learning is often lost. Rather than learn from clash occurrences and proactively work 562 to mitigate them, members of the PMT take a short-term reactive approach to identifying and 563 resolving them. Part of the problem is that clash detection software for example, currently lacks 564 automation and requires a labour intensive (and costly) analysis and post-investigation of clash 565 data by the BIM manager/ coordinator. For an entire project (dependent upon scope), design 566 clashes alone could equate to several tens of thousands of observations and in the longer term, 567 such an approach is untenable. Members of a fragmented design team were also observed to be 568 working in isolation and with bespoke BIM authoring platforms. Although IFCs were meant to 569 overcome this issue, errors with IFCs still doggedly persist.
However, there were still more than 400 clashes found between the structural model and the MEP model REF .
Origins and probabilities of MEP and structural design clashes within a federated BIM model
Automation in Construction
Computer Science