The quality management in precast concrete production and delivery processes supported by association analysis

Nicał, Aleksander; Anysz, Hubert

doi:10.1007/s13762-019-02597-9

Cited by 15 publications

(10 citation statements)

References 17 publications

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“…Finding them contributes to predictive features of association analysis. It was utilized in multiple disciplines such as: Meteorology, e.g., for rainfall predictions [ 46 ]; Biology [ 47 , 48 , 49 ]; Medicine [ 50 ] Social sciences [ 51 ]; Sales [ 41 ]; Insurance industry [ 52 ]; Quality management [ 53 ]; Construction risk assessment [ 54 ]; Bid-rigging detection [ 55 ]. …”

Section: Methodsmentioning

confidence: 99%

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

Rogala

Narloch

2021

Materials

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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.

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Section: Methodsmentioning

confidence: 99%

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

Rogala

Narloch

2021

Materials

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“…precipitation prediction [33]; -insurance risk assessment [34]; -traffic safety analysis [35][36][37]; -assessment of construction project risk [27]; -assessment of risk in construction disputes [38]; -a variety of problems in biology [39][40][41]; -preferences' discovering in social sciences [42]; -collusion detection in tender procedures [43]; -quality management problem-solving in production [44].…”

Section: Association Analysismentioning

confidence: 99%

7-Score Function for Assessing the Strength of Association Rules Applied for Construction Risk Quantifying

2022

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There are several factors influencing the time of construction project execution. The properties of the planned structure, the details of an order, and macroeconomic factors affect the project completion time. Every construction project is unique, but the data collected from previously completed projects help to plan the new one. The association analysis is a suitable tool for uncovering the rules—showing the influence of some factors appearing simultaneously. The input data to the association analysis must be preprocessed—every feature influencing the duration of the project must be divided into ranges. The number of features and the number of ranges (for each feature) create a very complicated combinatorial problem. The authors applied a metaheuristic tabu search algorithm to find the acceptable thresholds in the association analysis, increasing the strength of the rules found. The increase in the strength of the rules can help clients to avoid unfavorable sets of features, which in the past—with high confidence—significantly delayed projects. The new 7-score method can be used in various industries. This article shows its application to reduce the risk of a road construction contract delay. Importantly, the method is not based on expert opinions, but on historical data.

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“…• quality management in a precast concrete elements production [64], • detection of bid-rigging in the construction industry [65], • construction project risk assessment [46], • traffic safety issues [66,67].…”

Section: Association Analysis-rules Finding Toolmentioning

confidence: 99%

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

2021

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A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees (DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.

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The quality management in precast concrete production and delivery processes supported by association analysis

Cited by 15 publications

References 17 publications

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

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

7-Score Function for Assessing the Strength of Association Rules Applied for Construction Risk Quantifying

Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

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