{"id":26243,"date":"2024-06-18T03:56:15","date_gmt":"2024-06-18T10:56:15","guid":{"rendered":"https:\/\/essential.construction\/news\/know-whats-under-ground-and-make-smarter-planning-decisions\/"},"modified":"2024-06-18T03:56:17","modified_gmt":"2024-06-18T10:56:17","slug":"know-whats-under-ground-and-make-smarter-planning-decisions","status":"publish","type":"post","link":"https:\/\/essential.construction\/news\/know-whats-under-ground-and-make-smarter-planning-decisions\/","title":{"rendered":"Know What\u2019s Under Ground and Make Smarter Planning Decisions"},"content":{"rendered":"

\"-\"<\/a>
\n<\/p>\n

\n

A Finnish experimentation project developed a framework for classifying ground conditions for building and infrastructure construction. It will help anticipate the future cost of foundation laying during the early stages of city planning.<\/strong><\/p>\n

<\/p>\n

The ground
\nconditions of an area can have a substantial effect on the costs and the
\nenvironmental impacts of constructing buildings and infrastructure. At early
\nstage, urban designers don\u2019t typically have enough data to make smart decisions
\nabout zoning in that respect as obtaining that data is time-consuming and hence
\nalso costly.<\/p>\n

Consequently, an experimentation project called MAKU-digi: Making the costs of land use visible<\/em> devised a method for automating the analysis of ground conditions. <\/em>I had the pleasure of interviewing Juha Liukas,<\/strong> Lead Advisor at Sitowise<\/a>, and Hilkka Kallio<\/strong>, Geologist at Geological Survey of Finland<\/a> (GTK), about the project.<\/p>\n

Brewing the Idea<\/h2>\n

\u201cWhile we
\nwere developing the Citycad software back in the 1990s, we had this idea of
\ncombing a ground conditions map with a town plan for analyzing constructability,\u201d
\nsays Liukas. \u201cOne of our clients was the city of Espoo, which had just mapped
\nout the city\u2019s ground conditions.\u201d<\/p>\n

However, turning
\nthis idea into a method and a practical tool did not materialize until much
\nlater. In early 2017, Sitowise, Geological Survey of Finland, and six other
\norganizations started an experiment as part of the national KIRA-digi
\ndigitalization program. The project was called MAKU-digi.<\/p>\n

Espoo had
\nmeticulously kept its ground conditions data up to date and was invited to take
\npart in the project. Other large cities\u2014Helsinki, Vantaa, and Tampere\u2014 soon followed
\nsuite, together with the Finnish Transport Agency. They provided the project with
\nfive pilot case studies.<\/p>\n

The Sources of Ground
\nConditions Data<\/h2>\n

As ground
\nconditions data is not readily available in every part of the country, cities use
\nlocal geotechnical investigations to augment data from national sources.\u00a0<\/p>\n

\u201cAt GTK, we
\ncarried out a 35-year mapping project of superficial deposits, which created
\ngeographic data as polygons,\u201d says Kallio. \u201cThis gives an overview of soil
\nacross the country, 1-meter deep. I think, in the beginning, soil mapping was
\nmainly meant to serve agriculture and forest planning. Today our soil maps are
\nwidely used by land use planners\u201d<\/p>\n

Ground-penetrating
\nradars, satellite imagery, and drone surveys offer additional data that
\ngeological experts can use to estimate ground conditions. However, Kallio
\nemphasizes that geophysics does not offer alone accurate enough information for
\nconstruction purposes. She would rather rely on geotechnical investigations as
\nprimary data.<\/p>\n

\"Ground<\/figure>\n

Automating Geotechnical
\nAnalysis<\/h2>\n

A two-by-two
\nkilometer area can involve up to 10,000 individual geotechnical investigations.
\nAnalyzing that amount of data manually is impractical. Thus during the project,
\nGTK devised and tested a system that automatically identifies certain beds and
\nstrata in the ground.<\/p>\n

The other
\nmain output of MAKU-digi was a system for using geotechnical investigations,
\nsoil maps, and digital elevation models to classify any geographical location
\non a standardized scale. The \u201csoil construction capacity class\u201d of the
\nlocation, combined with the location\u2019s zoning elements, determines the
\nfoundation type. It, in turn, has a unit price that when multiplied with the
\nbuilding area gives an estimate of the foundation costs. The results can be
\npresented visually on a map.<\/p>\n

\u201cIn
\nMAKU-digi, we dealt with the relative rather than absolute costs of foundation
\nconstruction. There are other projects, like the national IHKU Alliance, that
\nwill provide a cost management system for infrastructure construction,\u201d Liukas
\npoints out. \u201cI envision a future where you can upload a draft detail plan to an
\nonline service and see the updated foundation costs at once, even as you make
\nchanges to the design.\u201d<\/p>\n

\"ground\"ground<\/figure>\n

Harmonizing Urban Design
\nData<\/h2>\n

\u201cThe
\nMinistry of the Environment saw our results and came to the conclusion that our
\nharmonized classification model could become a JHS recommendation,\u201d says
\nKallio. The so-called JHS recommendations provide national information
\nmanagement guidelines for both governmental and municipal administrations.<\/p>\n

Another
\nproject called Municipality Pilot is formulating a process and information
\nmodel for digital detail planning. It has tested combining a detail planning
\nmodel with the MAKU-digi analysis in three municipalities.<\/p>\n

\u201cI don\u2019t
\nknow of any examples from other countries where a ground condition method and classification
\nis standardized at the national level. So in that sense, we are doing
\npioneering work here in Finland,\u201d Kallio concludes.<\/p>\n

<\/div>\n