Artificial Neural Network Prediction of Compliance Coefficients for Composite Shear Keys of Built-Up Timber Beams.

This article explores the possibility of predicting the compliance coefficients for composite shear keys of built-up timber beams using artificial neural networks. The compliance coefficients determine the stresses and deflections of built-up timber beams. The article analyzes current theoretical methods for designing wooden built-up timber beams with shear keys and possible ways of applying them in modern construction. One of the design methods, based on the use of the compliance coefficients, is also discussed in detail. The novelty of this research is that the authors of the article collected, analysed, and combined data on the experimental values of the compliance coefficient for composite shear keys of built-up timber beams obtained by different researchers and published in other studies. For the first time, the authors of this article generated a table of input and output data for predicting compliance coefficients based on the analysis of the literature and collected data by the authors. As a result of this research, the article's authors proposed an artificial neural network (ANN) architecture and determined the mean absolute percentage error for the compliance coefficients kw and ki, which are equal to 0.054% and 0.052%, respectively. The proposed architecture can be used for practical application in designing built-up timber beams using various composite shear keys.

Multi-dichotomous sequencing method MDSM for ordering the importance of variants' properties in multi-criteria decision-making.

There are plenty of Multi-Criteria Decision-Making (MCDM) methods that help to choose the most suitable solution assessed by several criteria (e.g. Saaty 1990; Simos 1990; Pamucar et al. 2018). They are applied in cases where several scales of different units describe the variants or the variants' properties are represented by linguistic, non-numbered terms. The inherent part of the MCDM algorithms is calculating the weights of the variants' properties, necessary for ordering the variants. If - in a certain problem - there are several properties to consider, sequencing their importance becomes a problem itself. The innovative method of sequencing is proposed in the article based on dichotomous splitting of the properties' importance. If made several times, it leads to the coherent - internally and with the decision-maker's intention - order of the properties' importance. Then the weights of the properties can be calculated with the use of different MCDM methods. The description of the method can be shortened as follows:•Divide the full set of features into two dichotomous subsets of lower and higher importance•Continue dichotomous divisions until there are only the subsets containing one element or subsets containing elements of equal importance.

Designing the Composition of Cement-Stabilized Rammed Earth with the Association Analysis Application.

The main advantage of the structural composite material known as cement-stabilized rammed earth (CSRE) is that it can be formulated as a sustainable and cost-saving solution. The use of the aggregates collected very close to a construction site allows economizing on transportation costs. Another factor that makes sustainability higher and the costs lower is a small addition of cement to the CSRE in comparison to the regular concrete. However, the low cement content makes the compressive strength of this structural material sensitive to other factors. One of them is the composition of the aggregates. Considering the fact that they are obtained locally, without full laboratory control of their composition, achieving the required compressive strength of CSRE is a challenge. To assess the possibility of achieving a certain compressive strength of CSRE, based on its core properties, the innovative algorithm of designing CSRE is proposed. Based on 582 crash-test of CSRE samples of different composition and compaction levels, along with the use of association analysis, the spreadsheet application is created. Applying the algorithm and the spreadsheet, it is possible to design the composition of CSRE with high confidence of achieving the required compressive strength. The algorithm considers a random character of aggregates locally collected and proposes multiple possible ways of increasing the confidence. They are verified through innovatively applied association analyses in the enclosed spreadsheet.

Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools.

Cement-stabilized rammed earth (CSRE) is a sustainable construction material. The use of it allows for economizing on the cost of a structure. These two properties of CSRE are based on the fact that the soil used for the rammed mixture is usually dug close to the construction site, so it has random characteristics. That is the reason for the lack of widely accepted prescriptions for CSRE mixture, which could ascertain high enough compressive strength. Therefore, assessing which components of CSRE have the highest impact on its compressive strength becomes an important issue. There are three machine learning regression tools, i.e., artificial neural networks, decision tree, and random forest, used for predicting the compressive strength based on the relative content of CSRE composites (clay, silt, sand, gravel, cement, and water content). The database consisted of 434 samples of CSRE, which were prepared and crushed for testing purposes. Relatively low prediction errors of aforementioned models allowed for the use of explainable artificial intelligence tools (drop-out loss, mean squared error reduction, accumulated local effect) to rank the influence of the ingredients on the dependent variable-the compressive strength. Consistent results from all above-mentioned methods are discussed and compared to some statistical analysis of selected features. This innovative approach, helpful in designing the construction material is a solid base for reliable conclusions.

Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests.

Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures-often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy-over 95%-to attribute the results to the corresponding steel grade.

Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks.

Cement stabilized rammed earth (CRSE) is a sustainable, low energy consuming construction technique which utilizes inorganic soil, usually taken directly from the construction site, with a small addition of Portland cement as a building material. This technology is gaining popularity in various regions of the world, however, there are no uniform standards for designing the composition of the CSRE mixture. The main goal of this article is to propose a complete algorithm for designing CSRE with the use of subsoil obtained from the construction site. The article's authors propose the use of artificial neural networks (ANN) to determine the proper proportions of soil, cement, and water in a CSRE mixture that provides sufficient compressive strength. The secondary purpose of the paper (supporting the main goal) is to prove that artificial neural networks are suitable for designing CSRE mixtures. For this purpose, compressive strength was tested on several hundred CSRE samples, with different particle sizes, cement content and water additions. The input database was large enough to enable the artificial neural network to produce predictions of high accuracy. The developed algorithm allows us to determine, using relatively simple soil tests, the composition of the mixture ensuring compressive strength at a level that allows the use of this material in construction.