{"id":29315,"date":"2018-11-14T00:39:10","date_gmt":"2018-11-14T05:39:10","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-in-the-fragmented-construction-industry\/"},"modified":"2018-11-14T00:39:10","modified_gmt":"2018-11-14T05:39:10","slug":"machine-learning-in-the-fragmented-construction-industry","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-in-the-fragmented-construction-industry\/","title":{"rendered":"Machine Learning in the Fragmented Construction Industry"},"content":{"rendered":"

The construction industry represents 4% of the US GDP[1]<\/a>, and is one of the largest sectors of the world economy. However, it suffers from a slow rate of productivity growth at 1% per year in comparison to manufacturing that has a growth rate of 3.6% per year.[2]<\/a> The construction industry is also the lowest on the digitization scale[3]<\/a>, where technology and innovation spend is amongst the lowest of industries.<\/p>\n

The construction industry suffers from cost overruns and schedule delays that have become the norm. Mega projects and multi-billion-dollar capital programs with tight schedules are on the rise and companies are looking to technology to increase the efficiency of their building and information management processes.<\/p>\n

Machine learning (ML) applications have the potential to increase the efficiency of the construction process. The industry is one of the most fragmented in the world,[4]<\/a> which makes access to large data sets the biggest challenge for ML implementation. A large amount of structured data is required to train supervised learning algorithms. Organizations with access to large data sets are well positioned to take advantage of ML algorithms and advance the industry.<\/p>\n

Skanska is one of the largest construction and development companies in the world, operating in 11 countries, with 40,000 employees and a revenue of $18.8 billion in 2017 [5]<\/a>. The organization has access to a vast amount of project data that could be used to train ML algorithms and automate future tasks.<\/p>\n

\"\"<\/a>
Smarvid.io identifies worker on site and associated safety gear<\/figcaption><\/figure>\n

Skanska started implementing ML through the use of cloud-based platforms, such as Smartvid.io[6]<\/a>. On any given construction site, the field team collects a vast amount of data through photographs and videos. Historically, collecting, storing, and labeling all this photographic data has been a time-consuming task and construction firms often outsourced the task to consultants such as Multi-vista[7]<\/a>. The Smartvid.io platform structures the data collection of photos and videos from various sources including mobile, tablets and UAVs, provides unlimited storage and continuously learns from the user\u2019s labeling. That allows a supervised learning algorithm to learn from expert labeling and support the construction site safety team by automatically identifying safety risks from the photographs. For example, it is able to identify if a worker is not wearing a hard hat or not using the required safety gear.<\/p>\n

 <\/p>\n

Current applications in the industry are only scratching the surface of the potential of ML. A large portion of the industry still communicates through hard copy documents and spreadsheets. Multi-billion-dollar infrastructure projects teams are still more comfortable with email and spreadsheets rather than more structured project management systems. There is a lot of opportunity for improvement in construction and it really starts with data collection. Construction firms including Skanska have been mostly focusing on structuring the majority of the unstructured data to be able to implement ML. With technologies such as cloud-based project management systems and building information management software, organizations today have much more data to analyze and learn from.<\/p>\n

 <\/p>\n

Future applications of ML include: <\/strong><\/p>\n

    \n
  1. Automatically develop and check project estimates \u2013 construction cost estimating relies on the construction firm databases and staff experience. Estimators perform quantity take-offs, search company databases for similar projects and reach out to their trade contractors to identify the latest market price. The process is repetitive, time consuming and prone to human error. ML can support estimators in comparing cost structures and breakdowns across the firm\u2019s vast portfolio to increase the speed and accuracy of their estimating process.<\/li>\n<\/ol>\n

     <\/p>\n

    \"\"<\/a>
    ALICE utilizes machine learning to optimize construction schedules<\/figcaption><\/figure>\n
      \n
    1. Automatically develop project schedules \u2013 planners spend a significant portion of their time visualizing the sequence of construction activities and communicating their plan to the project team through Gantt charts and 4D models (time phased 3D models demonstrating construction sequence). By combining 4D models, video and photo capture of actual construction sequence, ML can automatically generate realistic schedules and generate 4D models by learning from a database of past projects.<\/li>\n<\/ol>\n

       <\/p>\n

      Opportunities for ML also include real-time performance measurement, supply chain management, generative design, and automating regulatory compliance checking through natural language processing applications.<\/p>\n

       <\/p>\n

       <\/p>\n

      In a fragmented industry plagued by adversarial contractual structures, there is little incentive for different companies to share data and advance ML. Technology companies are the emerging winners in the race. They are the impartial party collecting \u2018anonymous\u2019 data from all firms and training their ML algorithms. How can companies share data, remain competitive and capture value, without giving up their market share to technology firms?<\/p>\n

      (800 words)<\/p>\n

       <\/p>\n

      [1]<\/a> U.S. Department Of Commerce. Bureau Of Economic Analysis. Value Added By Industry As A Percentage Of Gross Domestic Product. 2018. https:\/\/apps.bea.gov\/iTable\/iTable.cfm?ReqID=51&step=1<\/a><\/p>\n

      [2]<\/a> McKinsey Global Institute. 2017. \u201cReinventing Construction: A Route To Higher Productivity\u201d Https:\/\/Www.Mckinsey.Com\/~\/Media\/Mckinsey\/Industries\/Capital%20projects%20and%20infrastructure\/Our%20insights\/Reinventing%20construction%20through%20a%20productivity%20revolution\/Mgi-Reinventing-Construction-A-Route-To-Higher-Productivity-Full-Report.Ashx<\/a><\/p>\n

      [3]<\/a>\u00a0 McKinsey & Company. 2016. Capital Projects & Infrastructure. \u201cImagining Construction\u2019s Digital Future\u201d. https:\/\/www.mckinsey.com\/industries\/capital-projects-and-infrastructure\/our-insights\/imagining-constructions-digital-future<\/a><\/p>\n

      [4]<\/a> World Economic Forum. 2016. \u201cShaping the Future of Construction\u201d. http:\/\/www3.weforum.org\/docs\/WEF_Shaping_the_Future_of_Construction_full_report__.pdf<\/a><\/p>\n

      [5]<\/a> Skanska. 2018. https:\/\/www.skanska.com\/<\/p>\n

      [6]<\/a> Caulfield, John. 2018. \u201cTech Report 5.0: AI Arrives\u201d. Building Design and Construction. https:\/\/www.bdcnetwork.com\/tech-report-50-ai-arrives<\/a><\/p>\n

      [7]<\/a> Multi-vista. 2018.\u00a0https:\/\/www.multivista.com\/construction-photo-documentation\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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