Yazar "Ayhan, Bilal Umut" seçeneğine göre listele

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    Alkali-activated basalt powder/slag systems: compressive strength and microstructural characterization
    (Springer, 2023) Akturk, Busra; Ayhan, Bilal Umut
    This study investigates the potential use of basalt powder as a sole precursor or blended in high amounts in slag-based alkali-activated systems. Eight alkali-activated mixes are prepared and comprehensively analyzed to determine the compressive strength development and microstructural characterization of basalt powder-based and basalt powder/slag blends activated by sodium hydroxide and a mixture of sodium hydroxide and sodium silicate. The mixes are characterized from a microstructural viewpoint via X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric and scanning electron microscopy analyses. The results show that while basalt powder-based mixes have low compressive strength values, ranging between 2 and 9 MPa, basalt powder/slag blend mixes exhibit a moderate compressive strength, i.e., 20 MPa at 28 days. Furthermore, sodium-silicate-activated basalt powder/slag blend mixes achieve high compressive strengths at early and further ages. The low strength values of the basalt powder-based mixes are attributed to the low concentration of reactive species and lack of Ca2+ ions in the medium, while the high compressive strength of the blended mixes is mainly associated with the formation of calcium aluminosilicate hydrate [C-(A)-S-H] or Na-enriched calcium sodium aluminosilicate hydrate [C-(N)-A-S-H] gel phases along with the calcic-plagioclase, which afford a denser microstructure. The obtained results show that basalt powder can be used in high concentrations, i.e., 50%, in alkali-activated systems, and basalt powder/slag blends can be a feasible, alternative binder system for use as a structural material.
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    An Association Rule Mining Model For The Assessment Of The Correlations Between The Attributes Of Severe Accidents
    (Vilnius Gediminas Tech Univ., 2020) Ayhan, Bilal Umut; Doğan, Neşet Berkay; Tokdemir, Onur Behzat
    Identifying the correlations between the attributes of severe accidents could be vital to preventing them. If such relationships were known dynamically, it would be possible to take preventative actions against accidents. The paper aims to develop an analytical model that is adaptable for each type of data to create preventative measures that will be suitable for any computational systems. The present model collectively shows the relationships between the attributes in a coherent manner to avoid severe accidents. In this respect, Association Rule Mining (ARM) is used as the technique to identify the correlations between the attributes. The research adopts a positivist approach to adhere to the factual knowledge concerning nine different accident types through case studies and quantitative measurements in an objective nature. ARM was exemplified with nine different types of construction accidents to validate the adaptability of the proposed model. The results show that each accident type has different characteristics with varying combinations of the attribute, and analytical model accomplished to accommodate variation through the dataset. Ultimately, professionals can identify the cause-effect relationships effectively and set up preventative measures to break the link between the accident causing factors.
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    Predicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR)
    (MDPI, 2022-11-26) Ayhan, Bilal Umut
    Abstract: Quality problems are crucial in construction projects since poor quality might lead to delays, low productivity, and cost overruns. In case preventive actions are absent, a lack of quality results in a chain of problems. As a solution, this study deals with non-conformities proactively by adopting an AI-based predictive model approach. The main objective of this study is to provide an automated solution structured on the data recording system for the adverse impacts of construction quality failures. For this purpose, we collected 2527 non-conformance reports from 59 diverse construction projects to develop a predictive model regarding the cost impact of the quality problems. The first of three stages forming the backbone of the study determines crucial attributes linked to quality problems through a literature survey and the Delphi method. Secondly, the Analytical Hierarchy Process (AHP) and a Genetic Algorithm (GA) were used to determine the attribute weights. In the final stage, we developed models to predict the cost impacts of non-conformities, using Casebased Reasoning (CBR). We made a comparison between the developed models to select the most precise one. The results show that the performance of CBR-GA using an automated weighting model is slightly better than CBR-AHP based on a subjective weighting system, whereas the case is the opposite in standard deviation in forecasting the cost outcome of the quality failures. Using both automated and expert systems, the study forecasts the cost impact of failures and reveals the factors linked to poor record-keeping. Ultimately, we concluded that the outcome of non-conformities can be predicted and prevented using past events via the developed AI-based predictive model.