{"id":1451,"date":"2020-08-25T10:34:28","date_gmt":"2020-08-25T10:34:28","guid":{"rendered":"https:\/\/sdac.tech\/?p=1451"},"modified":"2020-08-25T10:59:02","modified_gmt":"2020-08-25T10:59:02","slug":"ki-in-der-bauausfuhrungsplanung","status":"publish","type":"post","link":"https:\/\/sdac.tech\/en\/ki-in-der-bauausfuhrungsplanung\/","title":{"rendered":"AI in the construction planning"},"content":{"rendered":"
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AI in the construction planning<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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... first steps to analyze content using AI.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Our use case in the construction planning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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<\/p>

In construction planning we deal with the following three use cases:<\/span>\u00a0<\/span><\/p>

1. The review of service specifications<\/b>: In this use case, the current bill of quantities should be checked on completeness for the client in order to predict possible amendments. The goal is to support cost stability and to reduce annoying testing work.\u00a0<\/span><\/p>

2. The prediction of completion dates:<\/b>\u00a0Based on identified process sequences and their durations, options for project schedules will be created. The aim is to create easily a schedule that is based on empirical values and thus guarantees time stability.\u00a0<\/span><\/p>

3. <\/span>The prediction of deficiencies:<\/span><\/b>\u00a0This use case includes the prediction of deficiencies before the start of construction. The aim is to achieve early quality stability.<\/span><\/p>

In all three use cases, each construction site \u2013 even each project participant \u2013 u<\/span>ses free text o document information in different ways. <\/span>For example, when defects are recorded, a defect title and a defect description are usually documented. <\/span>A direct comparison of the contents is therefore not possible. And a preparation of the data with intelligent methods is required.
<\/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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Artificial intelligence for text processing<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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<\/p>

Using the example of deficiency prediction, a possible procedure for processing the information is explained below: <\/span><\/p>

Since there is no standardized wording for deficiencies, different site managers, for example, name the same deficiency differently. An evaluation is therefore impossible.\u00a0<\/span>\u00a0<\/span><\/p>

This is where Word\u00a0<\/span>Embeddings help us<\/span>\u00a0come into play. Words are represented by vectors in these procedures. One of the best known methods is word2vec. By vector representation, mathematical operations can be performed with words. Thus distances can be calculated and similar formulations can be recognized. With these\u00a0<\/span>Embeddings help us<\/span>\u00a0very good results can be achieved in Natural Language Processing (NLP) tasks such as\u00a0<\/span>Syntactic<\/span>\u00a0<\/span>Parsing<\/span>\u00a0or Sentiment Analysis.\u00a0<\/span>\u00a0<\/span><\/p>

With this method we reach the first important milestone in free text analysis. Individual words are classified and similarities are found. An evaluation of the data regarding deficiencies is thus made possible.\u00a0<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n \n \n \n\n \n Normal\n 0\n \n \n 21\n \n \n false\n false\n false\n \n DE\n DE\n AR-SA\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \/* Style Definitions *\/\n table.MsoNormalTable\n\t{mso-style-name:\"Normale Tabelle\";\n\tmso-tstyle-rowband-size:0;\n\tmso-tstyle-colband-size:0;\n\tmso-style-noshow:yes;\n\tmso-style-priority:99;\n\tmso-style-parent:\"\";\n\tmso-padding-alt:0cm 5.4pt 0cm 5.4pt;\n\tmso-para-margin-top:0cm;\n\tmso-para-margin-right:0cm;\n\tmso-para-margin-bottom:8.0pt;\n\tmso-para-margin-left:0cm;\n\tline-height:107%;\n\tmso-pagination:widow-orphan;\n\tfont-size:11.0pt;\n\tfont-family:\"Calibri\",sans-serif;\n\tmso-ascii-font-family:Calibri;\n\tmso-ascii-theme-font:minor-latin;\n\tmso-hansi-font-family:Calibri;\n\tmso-hansi-theme-font:minor-latin;\n\tmso-bidi-font-family:Arial;\n\tmso-bidi-theme-font:minor-bidi;\n\tmso-fareast-language:EN-US;}\n\n\nDie Anwendung von word2vec auf einen Datensatz zur M\u00e4ngelpr\u00e4diktion<\/p><\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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<\/p>\n

But not only in this use case Word <\/span><\/span>Embeddings help us<\/span><\/span>: Even for schedules and amendments there are almost without exception free text fields. With our construction-specific word vectors, these can also be understood by the machine.<\/span><\/span>
<\/span><\/p>

Here you will find a summary of the three use cases:
<\/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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<\/div>\t\t<\/div>\n\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
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<\/div>\t\t<\/div>\n\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
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<\/div>\t\t<\/div>\n\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<\/div>","protected":false},"excerpt":{"rendered":"

KI in der Bauausf\u00fchrungsplanung … erste Schritte zur Analyse der Inhalte mithilfe von KI. Unsere Anwendungsf\u00e4ll in der Bauausf\u00fchrungsplanung In der Bauausf\u00fchrungsplanung besch\u00e4ftigen wir uns mit folgenden drei Anwendungsf\u00e4llen:\u00a0 1. Der \u00dcberpr\u00fcfung von Leistungsverzeichnissen: In diesem Anwendungsfall soll f\u00fcr den Auftraggeber das aktuelle Leistungsverzeichnis auf Vollst\u00e4ndigkeit \u00fcberpr\u00fcft werden, um so eventuelle Nachtr\u00e4ge vorherzusagen. Das Ziel […]<\/p>","protected":false},"author":1,"featured_media":1458,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"_acf_changed":false,"ocean_post_layout":"","ocean_both_sidebars_style":"ssc-style","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"0","ocean_second_sidebar":"sidebar","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"0","ocean_custom_header_template":"0","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"0","ocean_menu_typo_font_family":"0","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"on","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"0","_jetpack_memberships_contains_paid_content":false,"ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"off","ocean_gallery_id":[],"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1451","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","entry","has-media","owp-thumbs-layout-horizontal","owp-btn-normal","owp-tabs-layout-horizontal","has-no-thumbnails"],"acf":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/sdac.tech\/wp-content\/uploads\/2020\/08\/word2vec.png?fit=1173%2C769&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/posts\/1451","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/comments?post=1451"}],"version-history":[{"count":10,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/posts\/1451\/revisions"}],"predecessor-version":[{"id":1463,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/posts\/1451\/revisions\/1463"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/media\/1458"}],"wp:attachment":[{"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/media?parent=1451"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/categories?post=1451"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sdac.tech\/en\/wp-json\/wp\/v2\/tags?post=1451"}],"curies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