ORCID Profile
0000-0002-7569-9173
Current Organisation
Chiang Mai University
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Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 2022
DOI: 10.1016/J.JENVMAN.2021.114036
Abstract: Raw water is a significant resource for industrial water usage, but this water is not directly suitable for use due to the presence of contaminants. Therefore, pre-treatment is essential. The treatment generates water treatment residue (WTR) which consists of silt, clay and undesirable components. Most WTR is conventionally disposed of in landfill. In addition, the presence of iron (Fe) and manganese (Mn) in groundwater can result in a reddish-brown color and undesirable taste and odour. A number of expensive and complex technologies are being used for the removal of such iron and manganese. Due to the high Al
Publisher: MDPI AG
Date: 12-05-2021
DOI: 10.3390/MA14102515
Abstract: Geopolymer (GP) has been applied as an environmentally-friendly construction material in recent years. Many pozzolanic wastes, such as fly ash (FA) and bottom ash, are commonly used as source materials for synthesizing geopolymer. Nonetheless, many non-pozzolanic wastes are often applied in the field of civil engineering, including waste iron powder (WIP). WIPs are massively produced as by-products from iron and steel industries, and the production rate increases every year. As an iron-based material, WIP has properties of heat induction and restoration, which can enhance the heat curing process of GP. Therefore, this study aimed to utilize WIP in high-calcium FA geopolymer to develop a new type of geopolymer and examine its properties compared to the conventional geopolymer. Scanning electron microscopy and X-Ray diffraction were performed on the geopolymers. Mechanical properties, including compressive strength and flexural strength, were also determined. In addition, setting time and temperature monitoring during the heat curing process were carried out. The results indicated that the addition of WIP in FA geopolymer decreased the compressive strength, owing to the formation of tetrahydroxoferrate (II) sodium or Na2[Fe(OH)4]. However, a significant increase in the flexural strength of GP with WIP addition was detected. A flexural strength of 8.5 MPa was achieved by a 28-day s le with 20% of WIP addition, nearly three times higher than that of control.
Publisher: MDPI AG
Date: 26-01-2022
DOI: 10.3390/INFRASTRUCTURES7020017
Abstract: Landslide incidents frequently occur in the upper northern region of Thailand due to its topography, which is mostly mountainous with high slopes. In the past, when landslides happened in this area, they affected traffic accessibility for rescue and evacuation. For this reason, if the risk of landslides could be evaluated, it would help in the planning of preventive measures to mitigate the damage. This study was carried out to create and develop a risk estimation model using the artificial neural network (ANN) technique for landslides at the edge of the roadside, by collecting field data on past landslides in the study areas in Chiang Rai and Chiang Mai Provinces. A total of 9602 data points were collected. The variables for forecasting were: (1) land cover, (2) physiographic features, (3) slope angle, and (4) five-day cumulative rainfall. Two hidden layers were used to create the model. The number of nodes in the first and second hidden layers were five and one, respectively, which were derived from a total of 25 trials, and the highest accuracy achieved was 96.74%. When applying the model, a graph demonstrating the relationship between the landslide risk, rainfall, and the slopes of the road areas was obtained. The results show that high slopes result in more landslides than low slopes, and that rainfall is a major trigger for landslides on roads. The outcomes of the study could be used to create risk maps and provide information for developing warnings for high-slope mountain roads in the upper northern region of Thailand.
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2021
Publisher: MDPI AG
Date: 10-2021
DOI: 10.3390/SU131910938
Abstract: The application of asphalt hot mix recycling is one challenge in sustainable road pavement research. In addition to the vast amount of research on the performance of recycled asphalt–concrete, the research on the frictional resistance of recycled hot mix asphalt is still limited. The effects of aged asphalt and aged aggregates on the skid resistance of recycled hot mix asphalt were investigated in this research. The aged asphalt and aged aggregates were carefully extracted from the field-reclaimed asphalt pavement, and the engineering and mechanical properties of aged and virgin aggregates were measured. The degradation of recycled hot mix asphalt was simulated using an accelerated polishing machine to mimic road surface abrasion. Accordingly, the initial and final skid resistances of the recycled hot mix asphalt were determined and correlated with the properties of the aged asphalt and aggregates. The initial skid resistance of recycled hot mix asphalt decreased with reductions in penetration and ductility of the blended asphalt. However, the changes in the blended asphalt properties contributed only small variations to the final skid resistances of the recycled hot mix asphalt. The gradations of recycled hot mix asphalt correlated only with the final skid resistances. The aggregate gradations controlled the characteristics of the final skid resistance since the covered binder was partially polished off from the road surface at this stage.
Publisher: MDPI AG
Date: 19-11-2021
DOI: 10.3390/MA14227017
Abstract: Supplementary cementitious materials have been widely used to reduce the greenhouse gas emissions caused by ordinary Portland cement (OPC), including in the construction of road bases. In addition, the use of OPC in road base stabilization is inefficient due to its moisture sensitivity and lack of flexibility. Therefore, this study investigates the effect of hybrid alkali-activated materials (H-AAM) on flexibility and water prevention when used as binders while proposing a new and sustainable material. A cationic asphalt emulsion (CAE) was applied to increase this cementless material’s resistance to moisture damage and flexibility. The physical properties and structural formation of this H-AAM, consisting of fly ash, hydrated lime, and sodium hydroxide, were examined. The results revealed that the addition of CAE decreased the material’s mechanical strength due to its hindrance of pozzolanic reactions and alkali activations. This study revealed decreases in the cementitious product’s peak in the x-ray diffraction analysis (XRD) tests and the number of tetrahedrons detected in the Fourier transform infrared spectroscopy analysis (FTIR) tests. The scanning electron microscope (SEM) images showed some signs of asphalt films surrounding hybrid alkali-activated particles and even some unreacted FA particles, indicating incomplete chemical reactions in the study material’s matrix. However, the H-AAM was still able to meet the minimum road base strength requirement of 1.72 MPa. Furthermore, the toughness and flexibility of the H-AAM were enhanced by CAE. Notably, adding 10% and 20% CAE by weight to the hybrid alkali-activated binder produced a significant advantage in terms of water absorption, which can be explained by its influence on the material’s consolidation of its matrices, resulting in significant void reductions. Hence, the outcomes of this study might reveal an opportunity for developing a new stabilizing agent for road bases with water-prevention properties and flexibility that remains faithful to the green construction material concept.
Publisher: Springer Science and Business Media LLC
Date: 05-06-2023
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 11-2020
Publisher: MDPI AG
Date: 08-04-2021
DOI: 10.3390/MA14081858
Abstract: With a lack of standard lateritic soil for use in road construction, suitable economical and sustainable soil-stabilization techniques are in demand. This study aimed to examine flue gas desulfurization (FGD) gypsum, a by-product of coal power plants, for use in soil–cement stabilization, specifically for ability to strengthen poor high-clay, lateritic soil but with a lower cement content. A series of compaction tests and unconfined compressive strength (UCS) tests were performed in conjunction with scanning electron microscope (SEM) analyses. Therefore, the strength development and the role of FGD gypsum in the soil–cement–FGD gypsum mixtures with varying cement and FGD gypsum contents were characterized in this study. The study results showed that adding FGD gypsum can enhance the strength of the stabilized substandard lateritic soil. Extra FGD gypsum added to the cement hydration system provided more sulfate ions, leading to the formation of ettringite and monosulfate, which are the hardening cementitious products from the cement hydration reaction. Both products contributed to the strength gain of the soil–cement–FGD gypsum material. However, the strength can be reduced when too much FGD gypsum is added because the undissolved gypsum has a weak structure. Examinations of FGD gypsum in the soil–cement–FGD gypsum mixtures by SEM confirmed that adding FGD gypsum can reduce the cement content in a soil–cement mix to achieve a given UCS value.
Publisher: MDPI AG
Date: 05-07-2021
DOI: 10.3390/S21134620
Abstract: Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.
Publisher: Elsevier BV
Date: 09-2021
Publisher: MDPI AG
Date: 20-03-2021
DOI: 10.3390/MA14061528
Abstract: The alkali-silica reaction (ASR) is an important consideration in ensuring the long-term durability of concrete materials, especially for those containing reactive aggregates. Although fly ash (FA) has proven to be useful in preventing ASR expansion, the filler effect and the effect of FA fineness on ASR expansion are not well defined in the present literature. Hence, this study aimed to examine the effects of the filler and fineness of FA on ASR mortar expansion. FAs with two different finenesses were used to substitute ordinary Portland cement (OPC) at 20% by weight of binder. River sand (RS) with the same fineness as the FA was also used to replace OPC at the same rate as FA. The replacement of OPC with RS (an inert material) was carried out to observe the filler effect of FA on ASR. The results showed that FA and RS provided lower ASR expansions compared with the control mortar. Fine and coarse fly ashes in this study had almost the same effectiveness in mitigating the ASR expansion of the mortars. For the filler effect, smaller particles of RS had more influence on the ASR reduction than RS with coarser particles. A significant mitigation of the ASR expansion was obtained by decreasing the OPC content in the mortar mixture through its partial substitution with FA and RS.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 09-2022
Publisher: MDPI AG
Date: 28-04-2022
DOI: 10.3390/MA15093181
Abstract: The worldwide demand for roads to serve global economic growth has led to the increasing popularity of road improvement using cement. This, in turn, has led to increased demand for cement and the associated problem of CO
Publisher: Elsevier BV
Date: 11-2023
No related grants have been discovered for Peerapong Jitsangiam.