Advances and Applications in Geospatial Technology and Earth Resources

Underground mining-induced land subsidence may cause serious damage to engineering structures (e.g., buildings or roads) therefore, it is necessary to predict the subsidence with the highest possible accuracy. This paper proposes a new method for estimating preliminary values of the parameters to the modified Knothe time function, resulting in an improved capability of predicting land subsidence. A computational tool incorporating the proposed method has been developed to practically and numerically facilitate the time-series prediction of mining subsidence. A case study at the Mong Duong colliery at Quang Ninh province in Vietnam was considered and back-analyzed to validate the capability and accuracy of the tool. The accuracy of the subsidence prediction was evaluated using Root Mean Square Errors (\( RMSE \)), Mean Absolute Errors (\( MAE \)), and the Correlation coefficient (\( r \)). The result showed that the proposed method predicted reasonably well both the calibrating dataset (\( RMSE \) = 15 mm, \( MAE \) = 13 mm, \( r \) = 0.996) and the validating dataset (\( RMSE \) = 44 mm, \( MAE \) = 37 mm, \( r \) = 0.857). Based on the comparison results, it is concluded that the developed tool incorporating the proposed method is suitable for predicting underground mining-induced land subsidence.

Recent technological innovations have led to the available of lightweight Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) photogrammetry that are successfully applied for 3D topographic surveys. However, application of UAV and SfM for complex topographic areas i.e. open-pit mine areas is still poorly understood. This paper aims to investigate and verify potential application of these techniques for generating Digital Surface Model (DSM) at open-pit coal mine area and assessing its accuracy. For this purpose, the Nui Beo open-pit coal mine located in northeast Vietnam is selected as a case study. Accordingly, a total of 206 photos were captured using DJI Phantom 3 Professional. In addition, 19 ground control points (GCPs) were established using Leica TS09 total station. The accuracy of DSM was assessed using root-mean-square error (RMSE) in X, Y, Z, XY, and XYZ components. The result showed that the DSM model has high accuracy, RMSE on the 12 calibrated GCPs for X, Y, Z, XY, XYZ is 1.1 cm, 1.9 cm, 0.8 cm, 2.2 cm, and 2.3 cm, respectively, whereas RMSE on the 7 checked GCPs is 1.8 cm, 2.4 cm, 3.2 cm, 3.0 cm, and 4.4 cm for X, Y, Z, XY, XYZ components, respectively. We concluded that small UAV and SfM are feasible and valid tools for 3D topographic mapping in complex terrains such as open-pit coal mine areas.

Maps of underground construction works, such as water pipes and water drainage systems are necessary for expansion of urban areas. For shallow depths, Ground Penetrating Radar (GPR) can provide high-resolution subsurface images. Electromagnetic velocity is crucial in time-to-depth conversion and imaging of the structures from the GPR section. Shielded common-offset antennas can work in city surroundings due to superior noise isolation properties. We have implemented new automatic/semiautomatic strategies to define the electromagnetic velocity and locations of the construction works by using common offset GPR data. In our approach, Kirchhoff migration is employed to image underground objects by correcting the locations of subsurface reflectors (i.e. diffractors, dips). The automatic technique helps define the velocity and position of an object or a diffractor by targeting high-valued data points in the maximum energy difference section, which is calculated from multiple migrated GPR sections of different velocities. When migrated correctly, a collapsed diffractor will contain the majority of its energy at the peak of the diffraction hyperbola. If migrated using the wrong velocity, the peak of the diffraction hyperbola will contain the least energy, with the rest of the energy smeared over migration artefacts. In the semiautomatic technique, the calculated velocities and positions from the first strategy can help interpreters in judging focused zones and under/over migration artefacts from different migrated GPR sections by using a limited velocity band. We applied the techniques to 2D/3D visualizations of underground pipes from one numerical model and a case study in Ho Chi Minh City, Vietnam.

The main aim of this research is to propose and evaluate a new hybrid intelligent approach (namely LSSVM-FA) based on Least Squared Support Vector Machines (LSSVM) and Firefly algorithm (FA) for flood susceptible modeling with a case study at a typical flood region in Central Vietnam. LSSVM and FA are current state-of-the art machine learning techniques that have rarely been explored for flood study. For this aim, a geospatial database of flood for the study area was constructed that consists of 76 historical flooded locations and 10 influencing factors. Using the database, the flood model was established using LSSVM, and then, the model was optimized where the best model’s parameters were determined using FA. The goodness-of-fit and the prediction capability of the proposed model were evaluated using Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The results showed that the proposed model performs well with the training data (AUC = 0.961) and the validation data (AUC = 0.934). Since the proposed model is better than benchmarks i.e. Neuron-fuzzy, support vector machines, and random forest, it could be concluded that the proposed model is a promising tool that should be used for flood modeling. The result from this research is useful for land-use planning and management at flood-prone areas.

Tidal flat is a special environment which is submerged during flood tide and exposing the air during ebb tide. Tidal flats in the north coast in Vietnam surfer diurnal tide with tide range varying from 0.3 m to 3.5 m. Along 350 km coastline of the study area, the diversity of constituent conditions forms various tidal flats with different characteristics. Soil moisture and surface roughness are the key parameters for the studies concerning tidal flat environment. These surface parameters of tidal flats can be investigated using inversion models with SAR data. This study applies the inversion method of the Oh model 2004 to estimate vertical surface roughness and soil moisture. Two Sentinel-1A SAR data are acquired and preprocessed to yield backscattering coefficients of VV and VH polarizations of C band. In condition of missing co-polarization data (HH) for fully applying the Oh inversion model, a calibration function is generated to calibrate estimated roughness and soil moisture. This is the first time that the surface roughness and soil moisture of tidal flats are successfully estimated from SAR data in the study area. Even now, the applications of the Oh model for estimating surface parameters of tidal flat using Sentinel-1A SAR images have not found yet in literatures. Vertical roughness after calibrating is estimated with an accuracy of 0.09 cm. The accuracy of estimated soil moisture of tidal flats is not assessed due to lacking the referent data. However, the estimated soil moisture is good correlation with the real environmental conditions in the study area.

One of passive sensor’s limitations is its high sensitivity to weather condition during image acquiring process. Consequently, the image is often affected by cloud cover. This phenomenon severely influences the completeness of land use/cover obtained from optical satellite imagery and make image processing more complicatedly. However, the pattern of pixel values based on the season and weather changes determined from substantial remote sensing data within a region can help to reconstruct the imagery data which was missed due to the presence of clouds. Taking advantage of datasets containing a substantial amount of multitemporal images, this study proposed a method to reconstruct missed imagery data caused by cloud cover based on relationship between air temperature, humidity, visibility, rainfall, normalized difference vegetation index, direct solar radiation, diffuse solar radiation, reflected radiation and spectral radiance of each pixel obtained by Beyesian Neural Network. The proposed method was applied to generate a cloud-free Landsat image. The results showed that pixels generated by the proposed algorithm are very similar to the actual pixels, especially in non-change area with percentage of correlation coefficients (R) over 0.99 is approximate to 91%. However, the similarity reduced in areas which changed significantly over time period, with the percentages of R over 0.99 are about 78%.

Cat Ba is one of the most well-known islands located in North Vietnam, which has been recognized as a biosphere reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2004. Despite the large potential carbon stocks in mangrove forests of Cat Ba, the mangrove ecosystem of this island has suffered severe deforestation and forest degradation due to the conversion to shrimp aquaculture. Monitoring mangrove forest changes plays an important role for effective mangrove conservation and management. The objectives of this study were to map the spatial distribution of mangrove forest and to assess their changes between 2010 and 2015 in Cat Ba Biosphere Reserve, Hai Phong city of Vietnam using ALOS PALSAR data and a GIS-based support vector machine algorithm. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected. Then, spatial distributions of mangroves were derived using the support vector machine classifier. The results showed that the ALOS-2 PALSAR for 2015 achieves the overall accuracy of 85% and the kappa coefficient of 0.81, compared with those of 81% and 0.77, respectively from the ALOS PALSAR for 2010. The mangrove forest areas in the Cat Ba Biosphere Reserve, Vietnam decreased by 15% from 2010 to 2015. This research shows the potential use of ALOS PALSAR data combined with machine learning techniques in monitoring mangrove forest changes in tropical and semi-tropical climates.

The main objective of this study is to detect and predict the urban area expansion at Hanoi, a typical urbanization city in Vietnam. For this purpose, firstly, temporal SPOT-5 images for years 2003, 2007, and 2011 were used to classify four land cover classes, open water, vegetation, barren, and residential area. Secondly, Impervious Surface Index (ISI) computed from the spectral bands of the above imagery. This index was then used to extract impervious surface information of the study area from residential area. Using the three derived land use/land cover maps, the area of land use/land cover types in the Hanoi area for years 2019 and 2027 were simulated and predicted using a Markov chain model. There results showed that the impervious surfaces of the Hanoi will increase 8.27% and 14.09% of total study area in 2019 and 2027, respectively. The results from this study provide valuable information to the local city planners in their urban planning and development.

This study attempts to produce 15-meter resolution land cover maps over Northern Vietnam in 2007 and 2015 using multi-temporal and multi-sensor data including ASTER, Landsat, and PALSAR mosaic based on a kernel-based probabilistic classification method. Other ancillary such as SuomiNPP nightlight image, OpenStreetMap road network and SRTM30 were applied for additional information supplement. A number of about 60,000 reference data was built by field GPS photos as well as visual interpretation using Google Earth for training and validation. Results showed that the overall accuracy of the land cover maps is 81% and 89% in 2007, 2015 respectively. The results indicated many changes in areas of land cover types between 2007 and 2015 in Son La hydropower dam area and in selected sites for forest gain detection. The analysis showed that water area demonstrated an increasing trend while cropland area presented a decreasing trend in Son La hydropower dam area; and forest area experienced a rising trend whereas grassland area indicated a declining trend in the other selected sites. The results introduced a new high-resolution regional land cover data in Northern Vietnam for environmental modeling or other regional studies.

This paper presents a change detection framework for Synthetic Aperture Radar (SAR) Image Time Series (ITS) based on Change Detection Matrix (CDM) approach. This framework allows the identification of changes on the ground in multiple scales. First, the Patch-based Change Detection Matrix (P-CDM) is proposed for the detection of changes in patch scale. Then changed regions and images acquired on dates related to the change occurrence are selected from P-CDMs. Finally, changes between selected images are defined with more details for each changed region in pixel scale by using Kullback-Leibler divergence between two Log-normal distributions. The proposed approach was illustrated by a time series including 11 ascending ALOS PALSAR images with resolution of 33.2 m × 28.4 m (range × azimuth) and polarization HH over Bat Xat district, Lao Cai province, and a part of Phong Tho district, Lai Chau province, Vietnam. There are different kinds of surface change in this test-site, such as: abrupt changes caused by flash floods and landslides, progressive changes due to plant evolution of rice terrace fields and forests. The experimental results have proven the effectiveness of the proposed framework.

In this paper, we propose a new scheme to analyze factors that affect outbreak of malaria using the Locally-Compensated Ridge Geographically Weighted Regression (LCR-GWR). Since malaria prevalence is location dependence, the relationships between natural and social-economic factors to the development and concentration of malaria hotspots have been investigated. The proposed method is applied to DakNong province, one of the most vulnerable areas to malaria risk in Vietnam due to the lack of social infrastructure and the limited accessibility to health services. Even though mitigation campaigns were launched in the last several years, the number of new cases was found increasingly and several hotspots are still remained. The result is compared to those of several local analyses of spatial collinearity. It has been shown that LCR-GWR considerably improves the model fit and is useful to determine several factors including NDVI, DEM, distance to residential areas, distance to road that are highly associated with malaria risks. The results of this study help measuring the incidence of malaria in the context of climate change and under the impact of change in people’s livelihoods.

In this study, we proposed a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility mapping at the Kon Tum Province, Viet Nam. Landslide affecting factors (slope angle, slope aspect, elevation, valley depth, land use, NDVI, soil type, lithology, distance to geology boundaries, and distance to faults), and 1404 past and current landslide locations have been first collected from the study area for generating training and testing datasets. Secondly, the hybrid model RFFT has been constructed for landslide susceptibility assessment using training dataset. Performance of the proposed RFFT model has been validated by analysis of the Receiver Operating Characteristic (ROC) curve and statistical indexes, and compared with a well-known landslide models namely Support Vector Machines (SVM) and the single FT. Results show that the proposed RFFT model has good performance for landslide susceptibility assessment. It has better predictive capability compared with well-known SVM model and single FT model. Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.

Slope failures depend strongly on the geotechnical properties of soils and rocks. Therefore, thresholds of rainfall-induced landslides vary according to changes in soil properties. This study assesses the effects of residual soil characteristics on 73 rainfall-induced shallow landslides along three main routes in Bac Kan province, Northeast Vietnam. All of these landslides occurred during or after torrential rains. Of the total number of landslides, 71 were shallow rotational and translational slides. They occurred in residual soils of silty sand (SM), silt with sand (ML), and elastic silt (MH), among which SM was the most dominant. Rain infiltration modeling and deterministic and probabilistic analyses of slope stability were used to assess the effects of soil permeability, strength, thickness of residual soils and slope excavation on rainfall-induced shallow landslides. The slopes constituted by the SM soil type were more vulnerable to rainfall-induced shallow landslides than the ML and MH types due to its higher mean saturated hydraulic conductivity. Shallow translational and rotational earth slides were dominant on cut slopes when the thickness of the residual soils was less than 1.5 m and greater than 2.5 m, respectively. Slope excavation in the study area decreased the factor of safety by 20%. Three thresholds of rain intensity-duration (ID) were defined for deterministic stability analysis (ranging between extremely high and low landslide hazard levels): I = 204.47 D^−1.073, I = 174.91 D^−1.039 and I = 133.34 D^−0.981.

Landslides generally occur during rainy season in Himalayas. Most of the landslides observed in the Uttarakhand part of Himalaya, India are of shallow nature. In the present study, we proposed a hybrid model Particle Swarm Optimization based Adaptive-Network-Based Fuzzy Inference System (PSOANFIS), which is a hybrid intelligent approach of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO), for spatial prediction of shallow landslides in part of Uttarakhand State. Firstly, a total of 1295 historical landslide events occurred in the area were identified and mapped from satellite images in conjunction with available historical data from reports to construct a landslide inventory map. In addition, 16 affecting factors (slope angle, slope aspect, elevation, curvature, plan curvature, profile curvature, lithology, soil, distance to lineaments, lineament density, land cover, rainfall, road networks, distance to roads, road density, river networks, distance to river, and river density) were taken into account for landslide spatial modeling. Datasets (training and testing) were then generated from the analysis of the collected data using GIS application. Thereafter, landslide model PSOANFIS was constructed using training dataset for spatial prediction of landslides. Performance of the proposed hybrid model has been compared with another benchmark landslide model namely Support Vector Machines (SVM). Lastly, the predictive capability of the hybrid model was validated using Receiver Operating Characteristic (ROC) curve and Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) indexes. The results of the present study show that the PSOANFIS model performed well for spatial prediction of rainfall induced shallow landslides, thus the PSOANFIS method can also be applied for the development of better landslide predictive models in other landslide prone areas of the world.

The ability of delivering accurate appraisal on landslide occurrences is of practical need for establishing land-use plans in regional scales. Backpropagation Artificial Neural Network (BpANN) has been demonstrated to be an effective tool for landslide spatial prediction. In this study, Batch-Training Back-Propagation Artificial Neural Network which is an integration of BpANN, batch-training strategy, and early stopping criteria is proposed for landslide spatial modeling. The employed early stopping criteria include the Generalization Loss (GL) criterion and the Quotient of Generalization Loss and Progress (QGP) criterion. In addition, BpANN training performance has been known to be highly dependent on various tuning parameters. This paper focuses on the parameter setting of BpANN regarding the investigated early stopping criteria. A Geographic Information System (GIS) database, collected from the mountainous regions in Northern Vietnam, is utilized as a case study. Experimental results show that GL criterion may result in an underfitted BpANN; meanwhile, QGP criterion can help to avoid overfitting. Based on experimental outcomes, several recommendations are put forward for future studies on landslide spatial modeling with batch-training BpANN.

In the present study, a hybrid approach of Random Subspace Ensemble (RSS) and Reduced Error Pruning Trees (REPT) has been proposed to create a novel hybrid model namely RSS-REPT for landslide susceptibility modeling of the Mu Cang Chai district, Yen Bai province of Vietnam where is affected by a number of landslides every year. For the development of model, a spatial database consisting of 248 historic landslide events and 15 affecting factors (slope, aspect, curvature, plan curvature, profile curvature, elevation, lithology, land use, distance to faults, fault density, distance to roads, road density, distance to rivers, river density, and rainfall), was constructed to generate training and testing datasets. The novel hybrid model was then constructed using training dataset for landslide susceptibility assessment, and its predictive capability was validated using Receiver Operating Characteristic (ROC) curve and Statistical Indexes (SI) analysis. Performance of this novel model has been compared with another popular model namely Support Vector Machines (SVM). Results indicate that its performance (AUC = 0.835) is higher in comparison to the SVM model (AUC = 0.804). Thus the RSS-REPT can be considered as one of the promising methods for better landslide susceptibility assessment of landslide prone areas.

The Tuy Hoa area is part of central coastal zone of Vietnam, which is commonly exposed to natural calamity including several types of geological hazards. The area is underlain by dominantly magmatic rocks and subordinated sedimentary units, which were formed during the Paleozoic to Early Cenozoic. These were variably covered by thin Cenozoic sedimentary cover including subordinate Quaternary volcanics and predominant fluvial, marine and aeolian deposits, which overlying most part of the area. All of the crystalized rocks and unconsolidated sedimentary units are variably affected by regional tectonic deformation, including extensive fracturing, faulting, subsidence and uplift, which were resulted from multiphase paleotectonic and neotectonic activities. Recent recorded earthquake also indicates active ground movement in the area. Local structural elements can be documented on the basis of numerous geological and morphological evidences and qualitative dating of the displaced Quaternary materials, which revealed significant local uplift, subsidence or displacement during recent time in the study area. The northern part of the area has been uplifted at the rate of at least 0.17 mm per year. In contrast, some areas such as Ban Thach River basin and Hao Son Lake are subsided significantly, in which Hao Son Lake area has subsided at the rate of ca. 0.96 mm per year. Incorporating the local uplift and subsidence with the predicted regional sea level rise scenario during next 100 years, most of the coastal area in the north of Tuy Hoa area will be slightly flooded under maximum 0.60 m of sea level rise compared with the prediction of regional ca. 0.77 m of sea level rise. In contrast, the tectonic subsidence will lead to serve flooding by sea water in which Hao Son Lake area will be submerged at least to the depth of 0.9 m and Ban Thach River basin will be also flooded deeply under the sea level during next 100 years. Other hazards including landslides, beach erosion are locally developed along the coastal zone and are controlled by bedrock inhomogeneity, zones of structural weakness, and active tectonic movement. Thus, basement architecture and bedrock fracturing within an area of active tectonic regime are major factors to influence landscape morphology and geological hazards in the coastal zone. Therefore, accurate identification and proper documentation of regional and local structural elements must be properly addressed in order to predict natural hazards, especially in the context of a predicted global sea level change.

In the 21st century, fresh water scarcity is perhaps one of the biggest challenges in many coastal regions worldwide due to the rapid population growth, fast urbanization and unpredictable impacts of global climate change. Given this context, the identification of groundwater status is a crucial task for sustainable groundwater use and management practices in coastal areas around the world. This work, conducted in coastal areas of Soc Trang province, is an effort to assess groundwater quality and its controlling factors in a coastal area of the Mekong Delta, Vietnam. In this study, we investigate groundwater quality based on chemical parameters, stable isotopes (δ¹⁸O, δ²H) and saturation indices (SI). The study showed that groundwater in the study area is mainly classified into four groups: Na-Cl, Na-Mg-Ca-HCO3, Na-Mg-Ca-HCO3-SO4 and Na-HCO3-Cl. Groundwater quality might be substantially controlled by the rock-water interaction, particularly by mineral dissolution and ion-exchange process. Further, the stable isotopes and saturation indices depict the origin of salt water presenting in the aquifers because of three factors, including paleo-saline water dissolution at deeper aquifers, seawater intrusion into shallow aquifers and saline water diffusion at middle aquifers. This result suggests that the characteristics of hydrogeology, inappropriate groundwater pumping activities and change of hydrological regimes might be the main driving forces of disturbance groundwater flow systems and expansion of saline boundary in the coastal areas of the Vietnamese Mekong Delta.

The soil contamination by heavy metals significantly damages the environment, human health, plants and animals, which has become a burning issue recently. Several types of technology have long been in use to remedy the heavy metal contaminated soil. Among of them, solidification/stabilization was widely adopted to manage metal-contaminated soils due to its relatively low cost, easy use, comprehensive strength, and high resistance to biodegradation. In this paper, common binders and the mechanisms of heavy metal-soil-binder interaction were introduced based on literature studies. In general, the effectiveness of S/S process was studied by strength and leaching characteristics. Firstly, the unconfined compressive strength of solidified metal-contaminated soils was evaluated in terms of heavy metal content, curing time, metal type, binder content, soil property, binder type and nature condition. Furthermore, some strength prediction formulas of cement solidified heavy metal contaminated soils were summarized. Subsequently, the performance of the leaching test for S/S products was presented and the influence factors were the same as those of unconfined compressive strength, except for one more factor—soil-solution contact time. Finally, the cases of large-scale contaminated site restoration in China were introduced, and the restorative effects of which achieved the expected objectives.

Quang An, Thua Thien Hue province, Vietnam, is one of the areas with great heavy-mineral potential. The heavy-mineral ore body distributes along the beach with the width of 300–800 m and the length of about 6,100 m. A total of 4,398 samples were collected vertically in a grid pattern from 585 bore holes covering an area of 2.882 km². The results indicate that the ore body is in marine-eolian sediments at Late Holocene (\( {\text{mvQ}}_{ 2}^{ 2- 3} \)). Useful heavy minerals were ilmenite, rutile, leucoxene, anatase, zircon and monazite which could be found in the intrusive and other rocks in the region. The total heavy minerals (THM) content in the bulk samples is not so high with average of 1.172%. The heavy mineral grains are small with the size of 0.05–0.25 mm and they are well liberated, rounded to sub-rounded. The average thickness of ore body is about 8.3 m, however, its variation is relatively stable with the coefficient of 39.28%. The average content of TiO₂ in ilmenites of 58.02% and ZiO₂ in zircons of 60.89% indicate that titanium-zirconium heavy minerals in Quang An area have relatively good quality. The proven reserves of total heavy minerals in study area were determined reliably with about 406.595 thousand tons, of which the measured mineral reserve is 68.177 thousand tons and the indicated mineral reserve is 338.418 thousand tons. This paper deals with the distribution and potential of heavy minerals in the study area in order to promote efficient mineral exploitation and mineral processing.

Modelling of strains and deformations in salt mine areas encounters considerable difficulties because of the varying strength properties of salt, complex morphological formation of dome deposits and rheological properties of salt. Due to such properties the impact of salt extraction increases over hundreds of years and accurate determination of strains at a given moment and place is burdened with high uncertainty. Numerical modelling is useful when the model is reduced to one or several chambers. A broader range considerably lowers the accuracy and efficiency of calculations in such models. Stochastic models allow for 3D modelling of an entire mining complex, provided the model has been parametrized in detail. The process of strain and deformation modelling was presented on the example of one of the biggest salt mines in Europe, where the volume of over 21 million m³ of salt deposit was extracted. The stochastic model could be parametrized thanks to the documented measurements results of panel convergence and levelling on the surface. The use of land subsidence inversion in the least squares method allowed to estimate the optimum values of the model parameters. The correctness of the evaluation was qualitatively and quantitatively confirmed graphically by comparing modelled and measured values of subsidence. The presented model can be applied in the future extraction projects for predicting strains and deformations for an arbitrary moment

A coupling analysis system was built considering the interaction between surrounding rock and tunnel support structures. Three indexes including the utilization coefficient of overall performance of the supporting systemη, coupling efficiency of support structures W, and deformation characteristics of tunnel support structures B, were taken into account in this research. The corresponding weight values was obtained using Analytic Hierarchy Process (AHP). In addition, the quantitative analysis method was used to evaluate the influence of degree of coupling between surrounding rock and support structures with the aim of effectively controlling the movement of surrounding rock. The 3D software ANSYS was used to analyze the Yang Ling tunnel in Wu Xi province, China as an example of the coupling analysis system. As the grade level of surrounding rock in Yang Ling tunnel increases from type III to type V, it can be seen that under the condition of type IV the coupling efficiency of surrounding rock and supporting structure was the highest. The obtained results indicated that the method is appropriate for design optimization of tunnel support and studying the interaction between surrounding rock and support structures.

There is a large quantity of construction waste with low utilization in China. The construction and demolition (C&D) waste can be manufactured into recycled aggregate instead of natural aggregate, made into cement fly-ash recycled aggregate (CFRA) pile for foundation reinforcement. This study investigated the building demolition waste and construction waste production in Suzhou. The laboratory test was conducted to explore the unconfined compressive strength of CRFA pile samples with different condition, and the strength of samples met the design strength. The CFRA pile composite foundation is simulated to explore the relationship between influence factors (pile length, pile spacing, pile diameter, pile modulus) and settlement. The results indicate that the building demolition waste and construction waste production increases year by year, and waste concrete accounts for over 50% of building demolition waste. The length of pile has a great influence on the surface settlement of subgrade and foundation, and the settlement reduces effectively due to the strong bearing capacity of the pile in the soft soil layer. When the pile spacing is 3 times diameter of pile, the surface settlement at the center of the foundation is minimum. The increase of pile diameter and elastic modulus of pile yield to the decrease of the surface settlement of subgrade. The settlement of CFRA pile composite foundation meets the general control requirements (the post-construction settlement in the first-grade highway should be less than 300 mm).

During these recent years, there have been efforts to develop new potash mining projects in Thailand. However, society doubts whether these projects are worth developing. The government cannot respond well to this question since it has no tool for evaluating new projects and providing concrete answer on the issue. The purpose of this study is to establish a framework to assess the worthiness of new mining projects and apply it to one of the new potash mining projects to find answers for the society. The new assessment framework determines the project’s worthiness by a matrix of the necessity of project development and the negative impacts. The worthiness is presented in terms of development priority. The project in Bamnet Narong, Chaiyaphum province is selected as a case study. The project was classified as a moderate development priority. The results provide useful information for supporting governmental decisions, communicating with stakeholders, and identifying suitable management measures during the life cycle of the project.