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.
Running a tender procedure in the construction industry a client expects receiving reasonable prices in the tenders. However, the market competition and the contractors’ will to win a contract to work out a profit, sometimes make the prices far from expected. They can be really low, almost impossible to be kept during the contract execution. Oppositely, the offered prices can be far above a client’s expectation. If they are extremely high, a client has to decide, to accept one of them (even bid-rigging is suspected), or to cancel the procedure. Nevertheless, cancelling a procedure means the considerably postponed the start of the construction. The analysis of almost 400 completed tender procedures (in the Polish road construction industry) and the market trends proved that unexpectedly high prices do not always have to point the collusive behaviours of the offerors. As aforementioned analysis is based on past cases, there is a need to propose the predicting tool, to help clients making decisions in the new tender procedures: to accept the high price as a market-based, or to reject all of the offers as a bid-rigging is suspected. The above-mentioned analysis are based on the simple moving average tool. For predictions the autoregressive tool is proposed – long short-term memory (LSTM) neural networks.
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