Layered Model Research Articles

Keeping walls straight: data model and training set size matter for deep learning in building generalization

ABSTRACT Deep learning-backed models have shown their potential of conducting map generalization tasks. However, pioneering studies for raster-based building generalization encountered a common “wabbly-wall effect” that makes the predicted building shapes unrealistic. This effect was identified as a critical challenge in the existing studies. This work proposes a layered data representation model that separately stores a building for generalization and its context buildings in different channels. Incorporating adjustments to training sample generation and prediction tasks, we show how even without using more complex deep learning architectures, the widely used Residual U-Net can already produce straight walls for the generalized buildings and maintains rectangularity and parallelism of the buildings very well for building simplification and aggregation in the scale transition from 1:5,000 to 1:10,000 and 1:5,000 to 1:15,000, respectively. Experiments with visual evaluation and quantitative indicators such as Intersection over Union (IoU), fractality, and roughness index show that using a larger input tensor size is an easy but effective solution to improve prediction. Balancing samples with data augmentation and introducing an attention mechanism to increase network learning capacity can help in certain experiment settings but have obvious tradeoffs. In addition, we find that the defects observed in previous studies may be due to a lack of enough training samples. We thus conclude that the wabbly-wall challenge can be solved, paving the way for further studies of applying raster-based deep learning models on map generalization.

A new technique inversion Time-Domain electromagnetic data

Time-Domain Electromagnetic (TDEM) data modeling, especially for central-loop configurations, is often achieved through 1D inversion models. This study aims to enhance the accuracy and efficiency of TDEM data inversion by employing the Born Approximation method to address calculation and convergence speed issues. We also utilize the modified Symbiotic Organism Search (mSOS), a global optimization algorithm capable of handling multi-minimum problems in non-linear objective functions, to optimize the inversion process. Our research includes the assessment of the accuracy and performance of this approach through inversion modeling on both synthetic and field data. The accuracy of the synthetic data was evaluated based on the algorithm's capability to retrieve the values of the synthetic data, as indicated by the small relative error between the synthetic model parameters and the calculated model. In the case of field data modeling, the accuracy relied on the consistency achieved when modeling the data with different numbers of layers. Additionally, we considered the time required to perform the inversion as an evaluation metric for inversion performance. For the synthetic data case, the algorithm produced relatively accurate models with misfit values of approximately 0 % and low relative error values. In the field data case, the inversion models demonstrated consistency and reduced misfit values when the data was modeled with different numbers of layers, specifically 8.72 % for the 2-layer model, 3.92 % for the 3-layer model, and 2.61 % for the 4- and 5-layer models. Both datasets required less than 19 min for 10,000 iterations. These findings highlight the innovative nature of the mSOS algorithm and its potential for practical applications in TDEM inversion studies.

A Real-Time Permittivity Estimation Method for Stepped-Frequency Ground-Penetrating Radar by Full-Waveform Inversion

Ground-penetrating radar (GPR) has been widely used in estimating the permittivity of mediums. The radar echo amplitude method is an important method used by GPR in this estimation, the basic step of which is to deduce the magnitude of the permittivity according to the relationship between the reflection coefficient and the permittivity. Based on the basic principle of the radar echo amplitude method, this paper proposes a full-wave inversion real-time permittivity estimation method that can be used for stepped-frequency GPR (SFGPR), which offers high efficiency, accuracy, and generalization ability. The characteristics of this method are mainly reflected in the following four aspects: Using the SFGPR system and introducing a layered media detection model, we can complete waveform compensation optimization with high precision. The distance between the antenna and the surface of the reflective medium is extracted from the time domain waveform without manual measurement, avoiding human measurement errors. The inversion of the total reflection waveform at the required height works under the principle of an electromagnetic field, eliminating the need for repeated metal plate calibration experiments and improving work efficiency and waveform accuracy. In a continuous measurement line, the total reflection waveform inversion on each measurement point can be efficiently completed, and the change of permittivity on the measurement line can be obtained. To evaluate the feasibility of the proposed method, we performed experiments on a wall of known thickness, and the permittivity estimation was basically consistent with that of the dielectric probe, physical model calculation, and wall penetration. We also successfully applied this method to the dielectric property analysis of adobe samples. The results indicate that the proposed method can help grasp the condition of a measured medium, which can ensure the accuracy of detection and improve subsequent data processing efficiency.

Study on mechanical characteristics and damage model of layered sandstone after high temperature action

Rock engineering, which includes slopes, tunnels, and mines, often encounters stratified rocks. These projects are also frequently exposed to special environments of high temperatures, such as deep underground or fire-related conditions. It is of significant importance to conduct research on the damage characteristics and constitutive models of stratified rocks under high-temperature conditions to accurately reflect the influences of rock structure characteristics, geological conditions, and load effects on the damage and deformation characteristics of rock engineering. Under five temperature conditions (20, 200, 400, 600, 800 ℃), the intact sandstone rock samples and the layered sandstone samples are subjected to high-temperature treatment, followed by triaxial compression tests. Based on existing research on statistical damage constitutive models for rocks, a high-temperature layered rock statistical damage constitutive model is established by introducing the Weibull distribution function and high-temperature, bedding, and load coupling damage variables, under the condition that the microelement strength follows the Drucker-Prager (D-P) criterion. The results indicate that the peak strength, damage threshold, elastic modulus, and longitudinal wave velocity show a "U"-shaped trend with an increasing bedding angle, with an opening upwards. As the temperature increases, the anisotropy of the rock initially increases and then decreases, with obvious ductile characteristics after the temperature reaches 600 ℃. The analysis of damage threshold, stress-strain curve, and macroscopic failure morphology shows that the 60° dipping angle sandstone is prone to undergo compressive-shear failure along the weak plane of bedding, exhibiting low toughness mechanical characteristics. Theoretical curves of the statistical damage constitutive model for high-temperature rock are in good agreement with the Triaxial shear test curve of sandstone, which indicates that the constitutive model can reflect the stress-strain process of layered sandstone after high-temperature action, and verifies the applicability of the model. This model does not include unconventional mechanical parameters and can reflect the ductility, brittleness, and strength characteristics with clear physical meanings. The findings of the study can offer theoretical support for computing and numerically modeling rock mechanics after high-temperature action.

Improved geoelectrical resistivity monitoring for earthquake precursors near the Xinfengjiang reservoir of southern China, with a fixed Wenner array of downhole electrodes in four separated boreholes

Over the past five decades, a geoelectrical resistivity monitoring experiment using fixed electrode arrays has been conducted at Heyuan in the Guangdong Province of China for detecting possible earthquake precursors near the Xinfengjiang reservoir. A pair of surface quasi-Wenner arrays designed to monitor temporal resistivity variations have been used in two oblique directions of N27°E and N80°W with electrode spacings AB/MN of 997 m/315 m and 1032 m/328 m, respectively. To reduce the effects of near-surface environmental changes, an improved experiment has been introduced in August 1992, using a subsurface Wenner array in the direction of N80°W with electrode spacings AB/MN of 54 m/18 m, consisting of downhole electrodes in four horizontally separated boreholes at the same depth of 65 m. During the experimental period of August 1992 to December 1996, a continuously downward trend in apparent resistivity with the improved downhole array and a peculiar annual variation with the conventional surface arrays have been observed. For the same period, no notable earthquakes occur in and around the Xinfengjiang reservoir. The field tests indicate that the downward trend of apparent resistivity was due to the aging of one of the current electrodes. By using simple layered models, we develop a sensitivity analysis to design a monitoring array with minimum environmental effects and to explain the observed resistivity variations, such as a paradoxical resistivity behavior related with negative sensitivity. No significant resistivity variations, which could be attributed to earthquake activity of magnitude 3.4 or less in the study area, are detected. We prove that the two monitoring methods with the modified subsurface Wenner and the conventional surface quasi-Wenner arrangements can complement each other to efficiently estimate a change in the electrical properties of rocks and reduce the ambiguity in the interpretation of apparent resistivity data.

Computing Theoretical Seismograms from a Point Source in a Spherical Multilayered Medium

Abstract Computing theoretical seismograms from a point source in a given Earth model is essential for modeling and inversion of observed seismic waveforms for Earth’s structure and earthquake source parameters. Here, we derived the propagator matrices and source terms for a spherical multilayered Earth model using the exact earth flattening transformation. We found that their differences from their counterparts in horizontal layered media are inversely proportional to the nondimensional horizontal wavenumber and its higher order. In addition, all the source terms in a spherical layered model have a source-depth dependent scaling factor that differs from in a horizontal layered model by up to 6% for deep earthquakes. The surface displacement produced by a point source can be obtained in a similar form as in horizontal layered media. Computation of theoretical seismograms was implemented using the generalized reflection and transmission coefficients method. Numerical tests show that our formulae and implementation are correct and efficient for computing full-wave seismograms, including the permanent displacements, at teleseismic distances up to 100°. Individual seismic phases can be isolated and analyzed semianalytically because the generalized reflection and transmission method is used. Furthermore, our analytic expression of displacement in terms of the propagator matrices and source terms can be used to derive analytic derivatives of seismograms for full-wave waveform inversion.

Analysis of the Construction and Course Effectiveness of the Online Course Platform for College Piano in the Background of the Internet

Abstract Internet technology continues to develop and deepen, course learning methods present diversity, and piano courses are an important way for colleges and universities to carry out students’ musical quality training. In this paper, we first propose a behavioral sequence analysis method for predicting students’ online course performance in the context of the Internet for college piano online courses. The sequence-based prediction task is divided into two stages in total. In the first stage, the sequence characteristics of students’ online course learning behaviors are predicted by mining the potential expressions of the implicit layer through deep RNN. In the second stage, the support vector machine is used to create an optimal segmentation hyperplane. The SPC two-stage classifier is able to predict student course grades in order to identify students with learning crises in time. Then a layered MVC model is used to design the university piano online course platform system. Finally, the performance of the platform and the effectiveness of the course were tested. When the number of concurrent users reached 150, the success rate of the server in processing transactions remained at about 96%, and the pre-test and post-test of students’ piano scores showed that the mean value of score-reading skills was 34.32 in the pre-test and 45.63 in the post-test, with a mean value improvement of 11.31. The study showed that the online course client had high operational performance and the online course could effectively the study shows that the online lesson client has high performance and the online lessons is effective in improving the students’ piano performance.

Accurate prediction of the through plane effective thermal conductivity of GDB-MPL composite region in PEMFC with a layered model

In the field of proton exchange membrane fuel cell (PEMFC), precise determination of through plane (TP) effective thermal conductivity (keff) of the composite region of the gas diffusion backing and microporous layer (GDB-MPL) holds significant importance for effective hydrothermal management. This study presents the establishment of a keff prediction model for the composite region based on X-ray computerized tomography (XCT). The model incorporates various inputs, including the equivalent pressure PGDB, average fiber angle θ¯, average skewed angle ϕ¯, the distribution of carbon fiber volume fraction εC, MPL volume fraction εMPL and other relevant information derived from XCT scans. By leveraging these inputs, the model facilitates the accurate prediction of keff. To explore the impact of different solid contents in MPL, the composite region, comprising CeTech GDS210 carbon paper, was categorized into three groups: 0 wt% (no MPL), 10 wt% and 13 wt% group. The theoretical values computed by the developed model were then compared with experimental values. Impressively, all the results demonstrated variations within the range of 1.5%. Such consistency indicates a high level of agreement between the predicted and experimental keff values. Moreover, it was noted the accumulation of MPL within the inter-fiber contact region significantly impeded the expansion of this area under escalating pressure, resulting in a pronounced limitation on heat transfer compared to the additional heat flux pathway facilitated by MPL. This work provides a new idea for studying the intrusive effects of water and polytetrafluoroethylene etc. on GDB, thereby contributing to a more accurate simulation and the commercialization of PEMFC.

Characteristics of an Ising-like Model with Ferromagnetic and Antiferromagnetic Interactions.

In the framework of mean field approximation, we consider a spin system consisting of two interacting sub-ensembles. The intra-ensemble interactions are ferromagnetic, while the inter-ensemble interactions are antiferromagnetic. We define the effective number of the nearest neighbors and show that if the two sub-ensembles have the same effective number of the nearest neighbors, the classical form of critical exponents (α=0, β=1/2, γ=γ'=1, δ=3) gives way to the non-classical form (α=0, β=3/2, γ=γ'=0, δ=1), and the scaling function changes simultaneously. We demonstrate that this system allows for two second-order phase transitions and two first-order phase transitions. We observe that an external magnetic field does not destroy the phase transitions but only shifts their critical points, allowing for control of the system's parameters. We discuss the regime when the magnetization as a function of the magnetic field develops a low-magnetization plateau and show that the height of this plateau abruptly rises to the value of one when the magnetic field reaches a critical value. Our analytical results are supported by a Monte Carlo simulation of a three-dimensional layered model.

Mechanism and parametric control of laser-layered paint removal for aircraft skin

Experimental and theoretical studies are conducted to clean the top coat and primer on the surface of aircraft skin separately in layers, and the morphological differences and elemental changes before and after paint removal are compared and analysed. By performing thermodynamic simulations, the top coat was removed by the thermal stress effect, and the primer by the combination of ablation and thermal stress effects. Finally, a layered paint-removal model is established and used to select the best laser parameters.

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

AbstractMonitoring surface and atmospheric parameters—like water vapor—is challenging in the Arctic, despite the daily Arctic‐wide coverage of spaceborne microwave radiometer data. This is mainly due to the difficulties in characterizing the sea ice surface emission: sea ice and snow microwave emission is high and highly variable. There are very few data sets combining relevant in situ measurements with co‐located remote sensing data, which further complicates the development of accurate retrieval algorithms. Here, we present a multi‐parameter retrieval based on the inversion of a forward model for both, atmosphere and surface, for non‐melting conditions. The model consists of a layered microwave emission model of snow and ice. Since snow scattering and emission effects, as well as temperature gradients, are taken into account, a high variability in brightness temperatures can be simulated. For ocean regions and the atmosphere existing parameterized forward models are used. By using optimal estimation, the forward model can be inverted allowing for the simultaneous and consistent retrieval of nine variables: integrated water vapor, liquid water path, sea ice concentration, multi‐year ice fraction, snow depth, snow‐ice interface temperature and snow‐air interface temperature as well as sea‐surface temperature and wind speed (over open ocean). In addition, the method provides retrieval uncertainty estimates for each retrieved parameter. To evaluate the forward model as well as the retrieval, we use the extensive data sets acquired during the year‐long Arctic expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (2019–2020) as a reference.

Which Attributes Should be Considered in Regulating the Internet of Things? Evidence From Conjoint Analysis

With rapid development in the Internet infrastructure, new and innovative services using the Internet of Things (IoT) have emerged. Despite the wide range of topics regarding IoT services that have been discussed, regulatory concerns solely concentrate on personal information and privacy protection in academic research and legislation. In other words, a comprehensive regulatory framework for IoT services should be developed with potential consumers in mind; however, relevant studies are lacking. To bridge this research gap, this study estimates consumer preferences to build an appropriate regulation for IoT service deployment. The study uses conjoint analysis and a layered model to investigate the importance of attributes for IoT services as part of the consideration supporting IoT regulations. Based on the estimation results, all attributes of the IoT services are relevant to potential consumers, and as an example of IoT applications, the Big Data analysis application is more critical to consumers in terms of marginal willingness-to-pay and relative importance. Furthermore, a new type of vertical integration is appealing to potential customers. Policymakers can benefit from the development of various IoT policies, simultaneous pursuits of IoT application innovation and privacy concerns, and nondiscriminatory regulation principles.

Adjacent tissues modulate shear wave propagation in axially loaded tendons

Shear wave tensiometry is a noninvasive approach for gauging tendon loads based on shear wave speed. Transient shear waves are induced and tracked via sensors secured to the skin overlying a superficial tendon. Wave speeds measured in vivo via tensiometry modulate with tendon load but are lower than that predicted by a tensioned beam model of an isolated tendon, which may be due to the added inertia of adjacent tissues. The objective of this study was to investigate the effects of adjacent fat tissue on shear wave propagation measurements in axially loaded tendons. We created a layered, dynamic finite element model of an elliptical tendon surrounded by subcutaneous fat. Transient shear waves were generated via an impulsive excitation delivered across the tendon or through the subcutaneous fat. The layered models demonstrated dispersive behavior with phase velocity increasing with frequency. Group shear wave speed could be ascertained via dispersion analysis or time-to-peak measures at sequential spatial locations. Simulated wave speeds in the tendon and adjacent fat were similar and modulated with tendon loading. However, wave speed magnitudes were consistently lower in the layered models than in an isolated tendon. For all models, the wave speed-stress relationship was well described by a tensioned beam model after accounting for the added inertia of the adjacent tissues. These results support the premise that externally excited shear waves are measurable in subcutaneous fat and modulate with axial loading in the underlying tendon. The model suggests that adjacent tissues add inertia to the system, which in turn lowers shear wave speeds. This information must be considered when using tensiometry as a clinical or research tool to infer absolute tendon loading.

A Hybrid Modified Deep Learning Architecture for Intrusion Detection System with Optimal Feature Selection

With the exponentially evolving trends in technology, IoT networks are vulnerable to serious security issues, allowing intruders to break into networks without authorization and manipulate the data. Their actions can be recognized and avoided by using a system that can detect intrusions. This paper presents a hybrid intelligent system and inverted hour-glass-based layered network classifier for feature selection and classification processes, respectively. To accomplish this task, three different datasets have been utilized in the proposed model for identifying old and new attacks. Moreover, a hybrid optimization feature selection technique has been implemented for selecting only those features that can enhance the accuracy of the detection rate. Finally, the classification is performed by using the inverted hour-glass-based layered network model in which data are up-sampled with the increase in the number of layers for effective training. Data up-sampling is performed when small subset of datapoints are observed for any class, which in turn helps in improving the accuracy of the proposed model. The proposed model demonstrated an accuracy of 99.967%, 99.567%, and 99.726% for NSL-KDD, KDD-CUP99, and UNSW NB15 datasets, respectively, which is significantly better than the traditional CNID model. These results demonstrate that our model can detect different attacks with high accuracy and is expected to show good results for new datasets as well. Additionally, to reduce the computational cost of the proposed model, we have implemented it on CPU-based core i3 processors, which are much cheaper than GPU processors.

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning.

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

Formulation and Evaluation of the Ballast Shear Interlocking Coefficient based on Analytical, Experimental, and numerical Analyses

The interaction among ballast particles, known as ballast shear interlocking (BSI), significantly influences the dynamic responses of railway track. This research presents a meticulously derived mathematical formulation for calculating BSI stiffness, which is then validated through experimental data obtained from a novel laboratory test procedure. The accuracy of the BSI formulation is demonstrated by its successful prediction of BSI properties in laboratory tests. Additionally, a numerical layered model is introduced to evaluate the sensitivity of the proposed formula to the mechanical and dynamic attributes of the ballasted track. This model highlights the resilience of tracks and emphasizes the critical role of BSI effects in railway track design, offering insights into parameters affecting track behavior. The study comprehensively explores ballast behavior, emphasizing the importance of BSI in track resiliency and advocating for its precise consideration in railway track design, modeling, and maintenance to ensure operational safety and efficiency. The developed methodology not only enhances the modeling of ballasted railway tracks as multi-layer systems but also advances the assessment of shear interlocking effects through laboratory testing.

Velocity model calibration for surface microseismic monitoring based on a 3D gently inclined layered equivalent model

Abstract Shale gas has become a major source of natural gas production and has received worldwide attention. Hydraulic fracturing is widely performed to stimulate oil and gas wells with considerable success. Given high-precision microseismic (MS) event locations, we can predict the development trend and region of fracturing and evaluate the stimulation effect, thereby providing technical guidance for subsequent exploitation. An accurate velocity model is essential for MS event positioning. However, simple velocity models, such as the uniform or vertical transverse isotropy (VTI) velocity model, are generally applied to calibrate the velocity model. Despite calibration, the VTI model may still face challenges in obtaining accurate MS event locations. Based on the structural characteristics of shale, we propose a novel local velocity model calibration algorithm for surface MS monitoring. To calibrate the velocity model, the actual strata interfaces are replaced with 3D gently inclined planes. We use very fast simulated annealing to concurrently tune the velocity, depth, and angle parameters of the model. Through the assessment of both the stacked amplitude at the position of the perforation shot and the relocation error of the perforation shot, we determine the ideal velocity model. To evaluate the effectiveness of our approach, we conduct experiments on both a synthetic model and a field dataset, and statistically analyze the location error. The results show that the proposed method obviously reduces the perforation shot relocation error and is well-suited for calibrating velocity models that are close to slightly inhomogeneous layered media.

Nonlinear seismic response analysis of layered seabed considering seawater-seabed coupling effects

Earthquake in the sea area is an important factor affecting the safety of marine engineering construction, seabed site seismic response analysis is an important preliminary work for marine engineering construction. Based on a fluid-solid weak coupling model which could simulate seawater-seabed interaction, four typical borehole sections along the proposed tunnel at Qiongzhou strait are selected to establish layered seabed models for studying the seabed site seismic responses affected by the seawater, seabed soft sediments and bedrock earthquake motion under bidirectional seismic excitation, in which the dynamic nonlinearity of the seabed soft soil is simulated by a generalized non-Masing constitutive model (DCZ model). The result shows: the suppression effect of seawater on seabed seismic motion exists only in the shallow range of seabed (< 50 m), and the suppression effect on the vertical seismic response is higher than that along the horizontal direction; the suppression effect of seawater on the seabed surface seismic motion and the frequency response phenomenon of “high frequency suppression, low frequency amplification” of seabed seismic response is positively correlated with seawater depth; The mean lines of the horizontal and vertical spectrum β obtained by numerical calculation are higher than the design spectrum in the land code within several period ranges, and the possibility of adverse effects induced by seawater and seabed soft sedimentation on the seismic resistance of marine engineering should be considered.

Heat extraction calculations for deep coaxial borehole heat exchangers: matrix analytical approach

SUMMARY Deep boreholes represent a source of clean energy. Therefore, effective calculations of potential extraction of heat from boreholes for realistic models of the Earth’s crust with variable thermal conductivity and diffusivity are needed. We deal with heat extraction in a quasi-steady state from coaxial boreholes where downward and upward flows of pumped fluid (water) are separated by an inner pipe and connected only at the bottom. We first obtain theoretical estimates of heat extraction for a thermally isolated inner pipe and a model of the ground with constant thermal diffusivity and conductivity. Then, we develop a new analytical matrix method for a general layered ground model that enables us to include depth-dependent ground properties as well as heat exchange between the downward and upward flows of fluid in the borehole. Our straightforward and fast approach is thus suitable for various parametric studies or as a tool for benchmarks of numerical software. A key role in heat extraction from coaxial boreholes is played by the inner-pipe thermal resistance. We apply our method to the parametric study showing the dependence of pumped water temperature and total heat extraction from the borehole on realistic borehole geometries under different amounts of water pumping. The calculations are performed for a 3 km deep borehole as the representative of present deep boreholes used for extraction of geothermal energy and for a 10 km deep borehole. Drilling of such a superdeep borehole has just started in China and our results demonstrate potential limits of geothermal energy extraction from such great depths.

Space-consciousness layered model as a regeneration approach for rural industrial heritage integrating local planning: a case study on Shecun village in Nanjing, china

ABSTRACT Rural industrial heritage reflects the ongoing changes in natural environmental, socio-economic, and cultural historical processes in the countryside. In the context of urbanization, the study, conservation, and reuse of rural industrial heritage have become necessary to consolidate the local community identity and promote rural development. This study aims at establishing an intervention tool with general applicability to regenerate rural industrial heritage while integrating local sustainable development. Through a diagrammatic analysis of the natural and artificial environment, this paper combines three aspects via a layered approach: landscape analysis, urban morphology, and spatial cognition. A layered model of rural industrial heritage regeneration is established, with space and consciousness as its core. The empirical study, using the example of the Shecun lime kiln, demonstrates that the spatial-consciousness layered model (SCLM) clearly explains the core roles of transforming material elements as spatial power and the evolution of cognitive elements as consciousness power in the regeneration of rural industrial heritage. It is a systemic model with adaptable and sustainable analysis. SCLM not only provides indicative strategies but serves as a management tool for the regeneration practices of rural industrial heritage.