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Conventional scheduling often relies on generic probabilities that miss site-process details behind schedule deviations. This paper uses machine learning on concrete pouring requests to forecast acceptance likelihood and incorporate probabilistic time delays for rejected requests. Tested on a real-world case, it compares CPM durations with static buffers against the proposed method, showing about a 10% difference.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/data-driven-machine-learning-approach-to-integrate-field-submittals-in-project-scheduling/639387/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/data-driven-machine-learning-approach-to-integrate-field-submittals-in-project-scheduling/639387.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address in construction project scheduling?","Question",{"text":112,"@type":113},"It addresses the gap between data-rich construction environments and the limited use of that data for improving schedule estimates, which remain inaccurate because they often rely on generic probability assumptions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed approach use field data?",{"text":117,"@type":113},"It uses concrete pouring requests as a site data stream and builds a random forest predictive model to forecast the likelihood that these requests will be accepted.",{"name":119,"@type":110,"acceptedAnswer":120},"How are schedule delays handled when forecasts involve rejected requests?",{"text":121,"@type":113},"The method embeds an analysis that adds probabilistic time delays associated with the forecast outcomes for rejected requests, improving the estimated project duration.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},639387,1793083055,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Downloaded on 11/13/20from ascelibrary .org by University of Liverpool . Copyright ASCE . For personal use only; all rights reserved .  \nData-Driven Machine Learning Approach to Integrate Field Submittals in Project Scheduling  \nMohamad Awada 1 ; F. Jordan Srour, Ph. D.2 ; and Issam M. Srour, Ph. D. , A. M.ASCE 3  \nAbstract: Construction projects are data-rich environments. However, those data are usually captured for site-specific reasons, e.g., the filing and approval of inspection requests, with little regard to how they can be leveraged for improved project management. Typically, scheduling techniques rely on general probability estimates, which do not capture the details of the site processes causing schedule deviations. This paper illustrates how machine learning techniques can mine project data to forecast delay in the midst of the project. The proposed method uses concrete pouring requests as an example of a site data stream and implements a random forest predictive model to forecast the likelihood of acceptance for these requests. Embedded in the proposed approach is an analysis that allows for the addition of probabilistic time delays associated with the forecast of rejected requests. The methodology was tested on a real-world case study, allowing for the comparison between a project duration estimate based on critical path method (CPM) with static buffers and a project duration obtained using the proposed method. The results show a difference of 10% between the two durations. The paper shows how using data streams from a construction site with machine learning techniques can enhance project duration estimates in execution. DOI: 10.1061/(ASCE)ME.1943-5479.0000873.© 2020 American Society of Civil Engineers.  \nAuthor keywords: Field submittals; Data analytics; Machine learning; Scheduling.  \nIntroduction  \nModern construction sites have an abundant amount of data including engineering data from the planning and design stages, accounting and job progress data, and field data collected by wearables, mobile devices, and sensors on equipment/materials (Bilal et al. 2016) . However, it is postulated that 96% of the data collected during construction goes unused (Snyder et al. 2018) . Furthermore, despite the large effort invested in construction scheduling to reduce delays, construction projects are still experiencing an average schedule overrun of 42 .7%(Ansar et al. 2016), which necessitates the search for novel tools to strengthen construction managers’ decision making and improve the accuracy of schedule estimates. Data analytics has the potential to change that reality (Wu et al. 2014) .  \nVarious techniques have been developed to help project managers schedule the required project work according to the prescribed timeline. In the construction field, the critical path method (CPM) has been widely accepted as the main project planning technique. Typically, the CPM relies on network techniques to ascertain the project duration on the basis of task early and late start dates  \n1Research Assistant, Dept. of Civil and Environmental Engineering, American Univ. of Beirut, P.O. Box 1107-2020, Beirut 11-0236, Lebanon. [Email: mohamadawada14@gmail.com](Email: mohamadawada14@gmail.com)  \n2Associate Professor, Dept. of IT and Operations Management, Lebanese American Univ., P.O. Box 13-5053, Chouran, Beirut 1102 2801, Lebanon. ORCID: [https://orcid.org/0000-0001-7623-723X. Email:](https://orcid.org/0000-0001-7623-723X. Email:)[ ](https://orcid.org/0000-0001-7623-723X. Email:)[jordan.srour@lau.edu.lb](jordan.srour@lau.edu.lb)  \n3Associate Professor, Dept. of Civil and Environmental Engineering, American Univ. of Beirut, P.O. Box 1107-2020, Beirut 11-0236, Lebanon (corresponding author). ORCID: [https://orcid.org/0000-0002-3498-6790](https://orcid.org/0000-0002-3498-6790) .  \nEmail: [is04@aub.edu.lb](is04@aub.edu.lb)  \nNote. This manuscript was submitted on February 17, 2020; approved on August 26, 2020; published online on November ","cbCaim3HCrC2uBon","https://ap.wps.com/l/cbCaim3HCrC2uBon","pdf",2646838,13,"English","# Introduction\n## Construction data utilization and scheduling challenges\n## Critical path method (CPM) and its limitations\n## Probabilistic scheduling alternatives (PERT, Monte Carlo, fuzzy models)","[{\"question\":\"What problem does the paper address in construction project scheduling?\",\"answer\":\"It addresses the gap between data-rich construction environments and the limited use of that data for improving schedule estimates, which remain inaccurate because they often rely on generic probability assumptions.\"},{\"question\":\"How does the proposed approach use field data?\",\"answer\":\"It uses concrete pouring requests as a site data stream and builds a random forest predictive model to forecast the likelihood that these requests will be accepted.\"},{\"question\":\"How are schedule delays handled when forecasts involve rejected requests?\",\"answer\":\"The method embeds an analysis that adds probabilistic time delays associated with the forecast outcomes for rejected requests, improving the estimated project duration.\"}]","Data-Driven Machine Learning Approach to Integrate Field Submittals in Project Scheduling | PDF",1792703389,33]