Bilateral Heterogeneity in an Upwelling Mantle via Double Subduction of Oceanic Lithosphere

Vietnam is a major field of Cenozoic volcanism in Southeast (SE) Asia. Two contrasting models have been proposed to explain the mantle upwelling and volcanism in this region; collision of the Indian and Eurasian continents or subduction of the Pacific or Indo-Australian oceanic lithosphere. To place constraints on the origin of the intraplate volcanism in SE Asia, new geochronological and geochemical data for Cenozoic basalts in Vietnam are presented. Based largely on Sr-Nd-Pb isotope systematics, it was found that the sources of basalts from Central and Southern Vietnam are chemically distinct forming a sharp boundary at 13.5°N. The basalts north of the boundary show isotopic features similar to Enriched Mantle type 2 (EM2) ocean island basalts. Whereas the basalts south of the boundary show isotopic features similar to Enriched Mantle type 1 (EM1) ocean island basalts. The EM1 and EM2 basalts display positive Sr anomalies and elevated Pb/Ce and Th/La ratios, respectively. Such features suggest the origins of the sources through the recycling of deeply-subducted crustal lithologies. Furthermore, subduction of dense oceanic lithosphere can induce a convecting cell in the upper mantle. Therefore, we suggest that the chemically different basalts from Central and Southern Vietnam represent the surface expression of melting in two different convecting cells, one is driven by subduction of the Pacific plate and the other by subduction of the Indo-Australian plate

Seismic stratigraphy of the central South China Sea basin and implications for neotectonics

Coring/logging data and physical property measurements from International Ocean Discovery Program Expedition 349 are integrated with, and correlated to, reflection seismic data to map seismic sequence boundaries and facies of the central basin and neighboring regions of the South China Sea. First-order sequence boundaries are interpreted, which are Oligocene/Miocene, middle Miocene/late Miocene, Miocene/Pliocene, and Pliocene/Pleistocene boundaries. A characteristic early Pleistocene strong reflector is also identified, which marks the top of extensive carbonate-rich deposition in the southern East and Southwest Subbasins. The fossil spreading ridge and the boundary between the East and Southwest Subbasins acted as major sedimentary barriers, across which seismic facies changes sharply and cannot be easily correlated. The sharp seismic facies change along the Miocene-Pliocene boundary indicates that a dramatic regional tectonostratigraphic event occurred at about 5 Ma, coeval with the onsets of uplift of Taiwan and accelerated subsidence and transgression in the northern margin. The depocenter or the area of the highest sedimentation rate switched from the northern East Subbasin during the Miocene to the Southwest Subbasin and the area close to the fossil ridge in the southern East Subbasin in the Pleistocene. The most active faulting and vertical uplifting now occur in the southern East Subbasin, caused most likely by the active and fastest subduction/obduction in the southern segment of the Manila Trench and the collision between the northeast Palawan and the Luzon arc. Timing of magmatic intrusions and seamounts constrained by seismic stratigraphy in the central basin varies and does not show temporal pulsing in their activities.Keywords: South China Sea, Neotectonism, Core-well-seismic integration, Seismic facies, Seismic stratigraphy, IODP Expedition 34

Ages and magnetic structures of the South China Sea constrained by deep tow magnetic surveys and IODP Expedition 349

Combined analyses of deep tow magnetic anomalies and International Ocean Discovery Program Expedition 349 cores show that initial seafloor spreading started around 33 Ma in the northeastern South China Sea (SCS), but varied slightly by 1-2 Myr along the northern continent-ocean boundary (COB). A southward ridge jump of ∼20 km occurred around 23.6 Ma in the East Subbasin; this timing also slightly varied along the ridge and was coeval to the onset of seafloor spreading in the Southwest Subbasin, which propagated for about 400 km southwestward from ∼23.6 to ∼21.5 Ma. The terminal age of seafloor spreading is ∼15 Ma in the East Subbasin and ∼16 Ma in the Southwest Subbasin. The full spreading rate in the East Subbasin varied largely from ∼20 to ∼80 km/Myr, but mostly decreased with time except for the period between ∼26.0 Ma and the ridge jump (∼23.6 Ma), within which the rate was the fastest at ∼70 km/Myr on average. The spreading rates are not correlated, in most cases, to magnetic anomaly amplitudes that reflect basement magnetization contrasts. Shipboard magnetic measurements reveal at least one magnetic reversal in the top 100 m of basaltic layers, in addition to large vertical intensity variations. These complexities are caused by late-stage lava flows that are magnetized in a different polarity from the primary basaltic layer emplaced during the main phase of crustal accretion. Deep tow magnetic modeling also reveals this smearing in basement magnetizations by incorporating a contamination coefficient of 0.5, which partly alleviates the problem of assuming a magnetic blocking model of constant thickness and uniform magnetization. The primary contribution to magnetic anomalies of the SCS is not in the top 100 m of the igneous basement.Keywords: Crustal evolution, Deep tow magnetic survey, South China Sea tectonics, International Ocean Discovery Program Expedition 349, Magnetic anomaly, ModelingKeywords: Crustal evolution, Deep tow magnetic survey, South China Sea tectonics, International Ocean Discovery Program Expedition 349, Magnetic anomaly, Modelin

Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance