A Satellite Data Mining Approach Based on Self-Organized Maps for the Early Warning of Ground Settlements in Urban Areas

Montisci, Augusto; Porcu, Maria Cristina

doi:10.3390/app12052679

Cited by 5 publications

(3 citation statements)

References 54 publications

(57 reference statements)

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“…The lowest energy of the L3SAT is presented in Equation ( 13) as this paper addresses logical rules that include three variables per phase. H min δL3SAT is calculated using Equation (13).…”

Section: -Satisfiability (L 3sat ) In Discrete Hopfield Neural Networkmentioning

confidence: 99%

“…In theory, data mining enables us to make an informed decision or to explore the outcome of a decision without making the decision itself. Thus, this method of handling data can be used in a multitude of real-life applications, including those in the medical [5], water research [6], stock market [7], data mining [8], landslide prediction [9], education [10], and diagnostics [11] fields, among others [12,13]. With the rapid advancement of science and technology, it is vital to return to the fundamentals of data mining.…”

Section: Introductionmentioning

confidence: 99%

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Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

et al. 2023

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Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics.

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Section: -Satisfiability (L 3sat ) In Discrete Hopfield Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

et al. 2023

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“…The analysis was used to observe and monitor the increase in ambient vibration levels over a long period during heightened heavy machinery work. Montisci and Porcu [52] proposed a neural-network-based tool for the early warning of ground settlement hazards in urban areas. Based on the analysis of MT-InSAR data through an unsupervised learning, the method found precursors of similar time-evolving phenomena.…”

Section: Introductionmentioning

confidence: 99%

Seismic Analysis of Historical Urban Walls: Application to the Volterra Case Study

2023

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Several Italian cities are characterized by the presence of centuries-old historic walls, which have a cultural heritage value and, due to their structural role as a retaining wall, often influence the safety of adjacent buildings and infrastructure. Ancient urban walls are increasingly subject to instability and collapse phenomena, because the greater frequency of extreme meteoric events aggravates the static condition of the walls and of the wall–soil system. Since the seismic risk in the contexts in which the historical urban walls are located is often medium-high, it is advisable to evaluate the influence of soil moisture on the seismic response of the soil–structure system. In this paper, the seismic vulnerability of historical urban walls was examined through considering scenarios of both dry and wet soil, in order to evaluate the seismic response of the structure as a function of soil imbibition. Seismic vulnerability analyses were carried out on the case study of the historical urban masonry walls of Volterra (Italy), which have been affected by two major collapses in the last ten years. Seismic vulnerability was assessed by means of the limit equilibrium method and the finite element method, and through adopting proper soil imbibition models. The results highlight which sections of the walls are at greater seismic risk due to the presence of soil moisture, as well as the influence of soil imbibition on the structural safety and failure mechanism.

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