140 Rust and Stoinski: Using Artificial Intelligence to Assist Tree Risk Assessment The overall impression in a tree assessment results from fuzzy facts that can only be described or esti- mated. Taking the example of wind load and its impact, such parameters might be exposure, crown area, shape, density, height, mechanical defects, and stress raisers. Provided this assessment is made by an experienced and knowledgeable person, we can assume that it has a high probability to be true. An expert will make many such estimates in the shortest possible time and relate them to each other intuitively and as best as possible. How does this work? The assessor doing a visual assessment does not think about specific measured values but rather considers different elements that can only be estimated and groups them together, e.g., wind load (site, size, and shape of the tree) and load-bearing capacity (stem and branch dimensions, mechanical defects, stress raisers). This process links together sup- posedly disjunctive values and makes them available as a basis for decision-making. This combination of nonmeasurable elements is done with a commercial AI software (Dylogos) that is already used in projects in other areas using the GDL. It builds causal chains to form a statement. The GDL enables this grouping through sets and the forma- tion of a causal chain. The sets are connected by means of operators, such as “AND” and “OR” and negation in natural linguistic statement chains that lead to an implication. The elements and the structuring are orig- inally defined by the user but can be modified and added to. The artificial intelligence will build and optimize the causal chains and the conclusions made. Connecting the software to an existing risk assessment database will help build the robust expertise that makes the conclusions reliable and facilitates the learning process of the AI part. How individual ele- ments are impacting the result or how the system gets to the result is transparent at any time. Assessing Tree Load (Simplified and Limited Model) Mechanical load is a key parameter for the assess- ment of the likelihood of failure of a tree or a part of it (Morgan and Cannell 1987; Cannell and Morgan 1989; Wessolly and Erb 1998; Bond 2011a, 2011b, 2012; Dahle et al. 2017). Unlike other outcomes of tree assessments, it is rated on an ordinal scale, as opposed to the logarithmic scales likelihoods are based on. To simplify the mathematical outline of the method further, we are limiting the description to a small subset of characteristics used to assess the load, which itself depends on several factors including exposure (wind zone, terrain, site), area (foliage, branches), and lever arm (tree height, branch length). All have in common that they are not precisely measurable. Defining a Fuzzy Set The assessment of the lever arm can be standardized by a mathematical procedure of fuzzy set formation. The parameter “lever arm” does not represent a quantity with an estimated value in the sense of fuzzy logic but rather is modelled as overlapping symmet- ric triangular distributions of truth-values, which are the classic representation of a fuzzy set (Figure 1). With the help of adjectives that function as conno- tations of the term “lever arm,” we can define its size. The adjectives “extreme,” “long,” “medium,” and “short” shall serve as examples. Figure 1. Set formation of lever arm. ©2022 International Society of Arboriculture
March 2022
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