{"id":1827,"date":"2020-11-04T16:49:30","date_gmt":"2020-11-04T16:49:30","guid":{"rendered":"https:\/\/sdac.tech\/?p=1827"},"modified":"2020-11-05T14:21:44","modified_gmt":"2020-11-05T14:21:44","slug":"human-machine-collaboration","status":"publish","type":"post","link":"https:\/\/sdac.tech\/en\/human-machine-collaboration\/","title":{"rendered":"Man and machine"},"content":{"rendered":"
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Why man and machine should work together in the construction industry<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Insights into our research<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Planning mistakes as motivation<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The construction of the new Berlin Airport (BER) and many other prestigious construction projects (e.g. the construction of the Eurotunnel or the Sydney Opera House (Hall 1982<\/a>)) show that planning mistakes by people often lead to delays in construction projects. Planning mistakes were first scientifically described by <\/span>Kahneman und Tversky (1977).<\/a> They show that when planning projects, people tend to underestimate the time needed to successfully complete the project.\u00a0The consequences of these misconceptions are usually increased costs, longer project lead times and reduced quality.<\/span><\/p>\n

However, these misjudgements are often not due to the lack of expertise of those responsible. Rather, systemic causes are at work:\u00a0The highly fragmented construction industry, the increasing complexity of construction projects and their limited time frame, result in a loss of transfer of knowledge and a lack of interfaces. An analytical overview of the project, without bias from previous projects, with constantly changing parameters, represents an enormous challenge for managers. This often results in poor management and bad decisions (Bent Flyvbjerg 2020<\/a>), which lead to the planning mistakes.\u00a0According to Kahneman und Tversky (1977).<\/a> a key lever to counteract this problem is to enable access to distributed information from a variety of projects. <\/p>\n

Due to the complexity of construction projects, many factors can affect decisions that must be documented as information in a project. For example, case studies in the forecast of project duration show that simple models, such as the Bromilowsche model of 1980, are not applicable in construction practice (Magnussen 2006<\/a>, Flyvbjerg 2002<\/a>). <\/span><\/p>\n

Further research shows that methods of Artificial Intelligence make better predictions because a large number of factors from a variety of projects can be evaluated mechanically (e.g. Petruseva 2012<\/a>, Dissanayaka 1999<\/a>, Wei 2006<\/a><\/span>). In these works mainly Artificial Neural Networks (ANN) were applied. ANNs operate according to the \"black box\" approach. They are not \"inherently\" comprehensible to humans like e.g. a decision tree. <\/span>Open, transparent \"glass box\" approaches are required for comprehensible decision-making.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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